This summary provides the high level information from the document in 3 sentences:
Tuberculosis is a major infectious disease that if left untreated can have high mortality rates, and while treatments exist diagnosis remains a challenge. The document discusses several methods for diagnosing tuberculosis including sputum smear microscopy, skin tests, and newer molecular diagnostic tests, as well as developing an automated method for detecting tuberculosis manifestations in chest radiographs. It proposes extracting the lung region from chest x-rays and then computing texture and shape features to classify the x-rays as normal or abnormal using a binary classifier in order to enable mass screening of large populations.
Pathomics Based Biomarkers and Precision MedicineJoel Saltz
Role of Digital Pathology Data Science (Pathomics) in precision medicine. Features from billions or trillions of objects segmented from digital Pathology data can be employed to predict patient outcome and steer treatment.
Presentation at Imaging 2020, Jackson Hole, WY September 2016
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
Generation and Use of Quantitative Pathology PhenotypeJoel Saltz
Motivation, tools and methods analysis of digital pathology imagery, integration with "omics" and Radiology, use in Precision Medicine. Presentation at the Early Detection Research Network meeting, April 2015, Atlanta GA
Tools to Analyze Morphology and Spatially Mapped Molecular Data - Informatio...Joel Saltz
Description of NCI Information Technology for Cancer Research Project dedicated to 1) development of development of Digital Pathology pipelines, databases, data modeling and visualization methods, 2) support for digital pathology/Radiology/"omics" based precision medicine
Presented at Spring 2015 Information Technology for Cancer Research PI Meeting
Pathomics Based Biomarkers and Precision MedicineJoel Saltz
Role of Digital Pathology Data Science (Pathomics) in precision medicine. Features from billions or trillions of objects segmented from digital Pathology data can be employed to predict patient outcome and steer treatment.
Presentation at Imaging 2020, Jackson Hole, WY September 2016
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
Generation and Use of Quantitative Pathology PhenotypeJoel Saltz
Motivation, tools and methods analysis of digital pathology imagery, integration with "omics" and Radiology, use in Precision Medicine. Presentation at the Early Detection Research Network meeting, April 2015, Atlanta GA
Tools to Analyze Morphology and Spatially Mapped Molecular Data - Informatio...Joel Saltz
Description of NCI Information Technology for Cancer Research Project dedicated to 1) development of development of Digital Pathology pipelines, databases, data modeling and visualization methods, 2) support for digital pathology/Radiology/"omics" based precision medicine
Presented at Spring 2015 Information Technology for Cancer Research PI Meeting
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
Machine Learning and Deep Contemplation of DataJoel Saltz
Spatio temporal data analytics - Generation of Features
1) Sanity Checking and Data Cleaning, 2) Qualitative Exploration, 3) Descriptive Statistics, 4) Classification, 5) Identification of Interesting Phenomena, 6) Prediction, 7) Control and 8)
Save Data for Later (Compression).
Detailed example from Precision Medicine; Pathomics, Radiomics.
Slides presented at the Molecular Med Tri-Con 2018 Precision Medicine, "Emerging Role of Radiomics in Precision Medicine" (http://www.triconference.com/Precision-Medicine/)
Abstract
The goal of this talk is to discuss the role of data standards, and specifically the Digital Imaging and Communication in Medicine (DICOM) standard, in supporting radiomics research. From the clinical images, to the storage of image annotations and results of radiomics analysis, standardization can potentially have transformative effect by enabling discovery, reuse and mining of the data, and integration of the radiomics workflows into the healthcare enterprise.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Digital Pathology: Precision Medicine, Deep Learning and Computer Aided Inter...Joel Saltz
In this presentation, I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe methods, tools and algorithms to extract information from Pathology images. These tools include ability to traverse whole slide images, segment nuclei, carry out deep learning region classification and characterize relationship between extracted features and morphological structures. I will also describe some of the research efforts that motivate development of these tools, the role Pathomics is playing in precision medicine research as well as the impact of Pathology Informatics on clinical practice and health care quality.
Presentation at the Department of Biomedical Informatics, University Pittsburgh Medical Center, April 27, 2018
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
A Novel Polymeric Prodrugs Synthesized by Mechanochemical Solid-State Copolym...inventionjournals
:We developed the novel polymeric prodrugs synthesized by mechanochemical solid-state copolymerization of glucose-based polysaccharides (dextran orglycogen) and the methacryloyloxy derivative of 5-fluorouracil (5-FU). The copolymerization proceededreadily and each polymeric prodrug was quantitatively obtained within8 h reaction. The number average molecular weight (Mn) and polydispersity (H) of the polymeric prodrug synthesized from dextran was 24,000 g/mol and 5.10, respectively. The number average particle diameter of the polymeric prodrug derived from glycogen was 14.9 nm. The hydrolysis profiles of the polymeric prodrug synthesized from dextranapparently followed the first-order kinetics, and 100% drug release was observed under the experimental condition used. The polymeric prodrug derived from glycogen also continued to release 5-FU at the first-order rate up to 5 h, followed by its rate constant decreased gradually. These results suggest that lower accessibility of water molecules for the synthetic polymer chains inside the glycogen particle might cause the gradual decrease of drug release rate.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
Machine Learning and Deep Contemplation of DataJoel Saltz
Spatio temporal data analytics - Generation of Features
1) Sanity Checking and Data Cleaning, 2) Qualitative Exploration, 3) Descriptive Statistics, 4) Classification, 5) Identification of Interesting Phenomena, 6) Prediction, 7) Control and 8)
Save Data for Later (Compression).
Detailed example from Precision Medicine; Pathomics, Radiomics.
Slides presented at the Molecular Med Tri-Con 2018 Precision Medicine, "Emerging Role of Radiomics in Precision Medicine" (http://www.triconference.com/Precision-Medicine/)
Abstract
The goal of this talk is to discuss the role of data standards, and specifically the Digital Imaging and Communication in Medicine (DICOM) standard, in supporting radiomics research. From the clinical images, to the storage of image annotations and results of radiomics analysis, standardization can potentially have transformative effect by enabling discovery, reuse and mining of the data, and integration of the radiomics workflows into the healthcare enterprise.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
Digital Pathology: Precision Medicine, Deep Learning and Computer Aided Inter...Joel Saltz
In this presentation, I will survey the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe methods, tools and algorithms to extract information from Pathology images. These tools include ability to traverse whole slide images, segment nuclei, carry out deep learning region classification and characterize relationship between extracted features and morphological structures. I will also describe some of the research efforts that motivate development of these tools, the role Pathomics is playing in precision medicine research as well as the impact of Pathology Informatics on clinical practice and health care quality.
Presentation at the Department of Biomedical Informatics, University Pittsburgh Medical Center, April 27, 2018
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Twenty Years of Whole Slide Imaging - the Coming Phase ChangeJoel Saltz
I surveyed the development of Digital Pathology methodology beginning with the 1997 virtual microscope prototype at Hopkins (PMC2233368) to current tools, methods and algorithms designed to display, analyze and classify whole slide imaging data. I will describe the capabilities of current methods, describe how these methods are likely to evolve and how they will be likely to impact Pathology research and practice.
A Novel Polymeric Prodrugs Synthesized by Mechanochemical Solid-State Copolym...inventionjournals
:We developed the novel polymeric prodrugs synthesized by mechanochemical solid-state copolymerization of glucose-based polysaccharides (dextran orglycogen) and the methacryloyloxy derivative of 5-fluorouracil (5-FU). The copolymerization proceededreadily and each polymeric prodrug was quantitatively obtained within8 h reaction. The number average molecular weight (Mn) and polydispersity (H) of the polymeric prodrug synthesized from dextran was 24,000 g/mol and 5.10, respectively. The number average particle diameter of the polymeric prodrug derived from glycogen was 14.9 nm. The hydrolysis profiles of the polymeric prodrug synthesized from dextranapparently followed the first-order kinetics, and 100% drug release was observed under the experimental condition used. The polymeric prodrug derived from glycogen also continued to release 5-FU at the first-order rate up to 5 h, followed by its rate constant decreased gradually. These results suggest that lower accessibility of water molecules for the synthetic polymer chains inside the glycogen particle might cause the gradual decrease of drug release rate.
Analysis of G+7 Multistoried Building for Various Locations of Shear Wall Con...ijceronline
Dynamic Analysis of a 8-storey building is done in this research paper and for this a irregular building has been taken into consideration. The research paper also considers the effect of change in the position of shear wall in the building plan and its effect on the structure during the analysis. Overall 8 cases of the various positions of shear wall has been considered separately and the comparative results has been seen. The building which we have taken for analysis is of irregular shape resembling the letter “L” in plan and has a height of 28.8 m. The dimensions are 25m along x direction and 30m along z direction. STAAD Pro v8i has been used for the dynamic analysis of 8 storied building. The analysis has taken into consideration the effect of torsion also and have been analysed as per IS 1893(Part 1): 2002. The building structure is considered to be made of M-20 grade concrete and Fe-415 grade steel. The factors considered for comparison are Peak storey Shear along both directions, Peak storey Shear along both directions considering torsion, Average Displacement along both directions and Drift along both directions. The structure considered is in Zone III in medium soil.
Carbon Stocks Estimation in South East Sulawesi Tropical Forest, Indonesia, u...ijceronline
This paper was aimed to estimate carbon stocks in South East Sulawesi tropical forest, Indonesia, using Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR). Two coherence Synthetic Aperture Radar (SAR) images of ALOS PALSAR full-polarimetric were used in this research. The research method is forming Random Volume over Ground (RVoG) model from interferometric phase coherence of two Full-Polarimetric ALOS PALSAR which temporal baseline is 46 days. Due to temporal decorrelation, coherence optimization was conducted to produce image with optimum coherences. The result showed that the RVoG forest height and carbon stocks which obtained from height inversion has a positive correlation with ground measurement.
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Graphical Model and Clustering-Regression based Methods for Causal Interactio...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
A review on detecting brain tumors using deep learning and magnetic resonanc...IJECEIAES
Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Intelligent algorithms for cell tracking and image segmentationijcsit
This research develop the managing within network and relationship mechanism in agribusiness
management through serious game. Agribusiness is represented as sand that work together in the market
(sandpile) to maintain networks and relationships. This research apply agent base model for predicting
activity network based on the parameters that exist in the collaboration. The result indicate that average
selling, average buying and market price (CK = 4) are not approach the value of the open market but
precisely coincide with eachother. Total bought and total sold are tend to be high value. This condition
suggests a very tight competition. The average selling, average buying and market price (CK = 0.01) are
approach the value of the open market. Total bought and total sold are not as high as total bought and total
sold, by using CK = 4, this condition shows the competition is not too tight.
Intelligent algorithms for cell tracking and image segmentationijcsit
Sensitive and accurate cell tracking system is important to cell motility studies. Recently, researchers have
developed several methods for detecting and tracking the living cells. To improve the living cells tracking
systems performance and accuracy, we focused on developing a novel technique for image processing. The
algorithm we propose presents novel image segmentation and tracking system technique to incorporate the
advantages of both Topological Alignments and snakes for more accurate tracking approach. The results
demonstrate that the proposed algorithm achieves accurate tracking for detecting and analyzing the
mobility of the living cells. The RMSE between the manual and the computed displacement was less than
12% on average. Where the Active Contour method gave a velocity RMSE of less than 11%, improves to
less than 8% by using the novel Algorithm. We have achieved better tracking and detecting for the cells,
also the ability of the system to improve the low contrast, under and over segmentation which is the most
cell tracking challenge problems and responsible for lacking accuracy in cell tracking techniques.
Similar to Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosise (20)
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosise
1. ISSN (e): 2250 – 3005 || Volume, 06 || Issue,12|| December – 2016 ||
International Journal of Computational Engineering Research (IJCER)
www.ijceronline.com Open Access Journal Page 66
Gaussian Multi-Scale Feature Disassociation Screening in
Tuberculosis
Ravi S Malasetty1
, Dr Sanjay Pande2
1
Research scholar, 2
Research supervisor
I. INTRODUCTION
Tuberculosis (TB) is the second most common cause of death from an infectious disease worldwide,
according to HIV,with a mortality rate of over 1.2 million people in 2010 [WHO-12]. TB is an infectious
disease. By the bacillus Myco bacterium tuberculosis caused the lung usually It spreads through the air when
people with active TB distribute coughing, sneezing or otherwise infectious bacteria. TB in Africa and South
east Asia, is wide spread poverty and malnutrition to reduce resistance to the disease most often. In addition,
opportunistic infections in immune compromised amplifies the problem of HIV/AIDS patients. [WHO-11] The
increasing incidence of multi-drug resistant TB was more an urgent need for an in expensive screening
technology to have to monitor the progress of treatment. Several antibiotics are available for the treatment of
TB.While mortality rate is high, if left untreated, antibiotic treatment improves the chances of survival. In
clinical studies, the curerates of over 90% have been documented [WHO-12]. Unfortunately, the diagnosis of
TB is still a major challenge.
The definitive test for TB is the identification of Myco bacterium tuberculosis in a clinical sputum
sample orpus, which is the current gold standard [WHO-12,GTC-11]. However, it may take several months to
identify these slow-growing organism in the laboratory. Another technique is sputum smear microscopy, in
which bacteria in sputum samples are observed under a microscope. This technique was developed more than
100 years ago [WHO-12]. In addition, to determine multiple skin tests on the immune response, whether an
individual is contracted TB available. Skin tests are not always reliable. The latest development for the detection
of molecular diagnostic tests is that are fast and accurate, and are highly sensitive and specific. However, further
financial support for these tests to be required common place [WHO-12, WHO-11, GTC-11]. In this work, we
present an automated approach to detect TB manifest at ions in chest radiographs (CXRS), based on previous
work in lung segmentation and lung disease classification [CJP-12,CPA-13, JKA et.al.,-12]. An automated
approach to reading X-ray allows mass screening of large populations that are not managed manually
II. LITERATURE SURVEY
Full understanding of the architecture of the brains along term goal of neuroscience. To achieve it,
advanced image processing tools are required, that automate the analysis and reconstruction of brain structures.
Synapses and mitochondria are two prominent structures with neurological interest for which various automated
image segmentation approaches have been recently proposed. In this work we present a comparative study of
several image featured escript or used for the segmentation of synapses and mitochondria in stacks of electron
microscopy images
Deciphering the architecture of the brain is a key challenge of science [Del-10]. In the last years we
have seen advances in the automated acquisition of large series of images of brain tissue [DH-04, KMW et. al.,-
08]. The analysis of these images enable the construction of detailed maps of neuron structures from which we
will better understand the basic cognitive functions of the brain, such as learning, memory and its pathologies
ABSTRACT
Tuberculosis is a major health threat in many regions of the world. When left undiagnosed and
consequently untreated, death rates of patients with tuberculosis are high. We first extract the lung
region using a lung nodule Edge detection method. For this lung region, we compute a set of texture and
shape features, which enable the x-rays to be classified as normal or abnormal using a binary classifier.
Thus, a development of edge detection solution to address these requirements can be implemented in a
wide range of situations. The general criteria for edge detection includes detection of edge with lower
rorrate ,whichmeans that the detection should accurately catch as many edges.
Keywords: Feature Descriptors, Rotation Variant feature, Tuberculosis
2. Gaussian Multi-Scale Feature Disassociation Screening In Tuberculosis
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[KL-10]. There are tools to manually analyze and segment the structures in such images. However, the
complexity of these images and the high number of neurons in a small section of the brain, makes the automated
analysis the only practical solution.
Mitochondria and synapses are two cell structures of neurological interest that are suitable for
automated processing. Synapses are the fundamental mechanism of communication between neurons.
Quantification of synapses, and the identification of its types and their distribution is critical to understand how
the brain works [BMRet.al.,-13]. Besides providing energy to the cell, mitochondria play an important role in
many essential cellular functions including signaling, differentiation, grow than death. The morphology and
distribution of Mitochondria has great importance in cellular physiology [CS-10] and synaptic function
[LLHet.al.,-07]. Also a typical morphologies or mitochondria distributions are indicative of abnormal cellular
states or the existence of neuro degenerative diseases[CNL-10].
Recent works have proposed algorithms for synapse [KSA et.al.,-11] and mitochondria segmentation
[GME-12, LSA et.al.,-12] employing various discriminating features. To extract these features some approaches
use general texture operators [KSA et.al.,-11], where as others employ specifically designed measurements
[LSA et.al.,-12]. In this work, we compare the features used in these works for the problem of joint
segmentation of synapses and mitochondria. In Figure6.1,we show a slice of one of the images used in our study
and its associated labels.
[Bol-79] The traditional methods of classification mainly follow two approaches: unsupervised and
supervised. The unsupervised approach attempts spectral grouping that may have an unclear meaning from the
user’s point of view. Having established these, the analyst then tries to associate an information class with each
group. The unsupervised approach is often referred to as clustering and results in statistics that are for spectral,
statistical clusters. In the supervised approach to classification, the image analyst supervises the pixel
categorization process by specifying to the computer algorithm; numerical descriptors of the various land cover
types present in the scene.
[Pat-72]Consequently, a prior ground data collection can be very time consuming. Also, the supervised
approach is subjective in the sense that the analyst tries to classify information categories, which are often
composed of several spectral classes whereas spectrally distinguishable classes will be revealed by the
unsupervised approach, and hence ground data collection requirements maybe reduced. Additionally, the
unsupervised approach has the potential advantage of revealing discriminable classes unknown from previous
work.
Many different aspects of human physiology, chemistry or behavior can be used for biometric
authentication. The selection of a particular biometric for use in a specific application involves a weighting of
several factors. [Cha-02] identified seven such factors to be used when assessing the suitability of any trait for
use in biometric authentication. Universality means that every person using a system should possess the
trait. Uniqueness means the trait should be sufficiently different for individuals in the relevant population such
that they can be distinguished from one another. Permanence relates to the manner in which a trait varies over
time. More specifically, a trait with 'good' permanence will be reasonably invariant over time with respect to the
specific matching algorithm. Measurability (collectability) relates to the ease of acquisition or measurement of
the trait. In addition, acquired data should be in a form that permits subsequent processing and extraction of the
relevant feature sets. Performance relates to the accuracy, speed, and robustness of technology
used. Acceptability relates to how well individuals in the relevant population accept the technology such that
they are willing to have their biometric trait captured and assessed. Circumvention relates to the ease with which
a trait might be imitated using an artifact or substitute.
3. Gaussian Multi-Scale Feature Disassociation Screening In Tuberculosis
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III. METHODOLOGY
Stage 1: Image Registration and Rectification:
Stage 2: Image Enhancement Techniques:
Stage 3: Image Fusion Techniques:
Stage 4: Classification Error Matrix:
Stage 5: Segmentation:
Stage 6: The Parameter Calculation as a criterion for Segmentation:
Stage 7: Neighborhood search-based method to omit isolated noise in the image:
Stage 8: Gabor Filters:
Stage 9: Geometric Methods and Template Matching:
IV. FEATURE DESCRIPTORS
In this section, we describe the feature descriptors considered in this study. We begin describing the
simple general purpose descriptors and proceed in order of increasing sophistication.
A. Simple Window and Histogram: We construct a simple window based descriptor ordering and
storing a vector of then × n neighborhood of the pixel that we want to describe. This naïve descriptor has
proved to be an excellent source of information for texture segmentation [VZ-03]. A histogram based
descriptor takes for each pixel an n×n neighborhood on which a gray level histogram is computed. In [LSA
et.al.,-12], a histogram and the Ray features [SCL-09] are used as elements of the feature vector form it
ochondria segmentation. In this wealso tested the histogram and the ray features separately.
B. Local Binary Patterns: The local binary patterns (LBP)[OPH-96],generate a binary code with
k digits taking in to account for each pixel pase to f k neighbor points at an r distance, wherer is the radius
from the central pixel p to its neighbors. If the value of p is higher than a neighbor ki then we insert a O In
the binary code, or 1ift he value is lower. The feature vectoris obtained from the histogram of the LBP
binary codes converted to its real values in an × n neighborhood. This process is out lined in Figure2.
Figure2. Computing Pixel Values of neighboring pixels:
Depending on the values of the neighboring pixels, the LBP generates a binary code that can be converted to a
real value. In this case the value would be: 0×1+0×2+1×4+1×8+0×16+0×32+0×64+1×128 = 148.
C. GRIMS: The GRIMS (Gaussian Rotation In variant and Multi-Scale) descriptors apply to each image in
the stack a set of linear Gaussian filters at different scales to compute zero, first and second order derivatives.
These linear operators are:
Figure 3. Illustration of Nearest Contour
Function c returns the position c from the nearest edge or cont our of the image I to the position m in direction
defined by the angleθ.
4. Gaussian Multi-Scale Feature Disassociation Screening In Tuberculosis
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Gσ ∗, σ · Gσ ∗
𝜕
𝜕𝑥
σ . Gσ ∗
𝜕
𝜕𝑦
σ2 ∗ Gσ
𝜕2
𝜕𝑥2
Gσ ∗
2
𝜕𝑥𝑦
σ ∗ ∂y2 (6.1)
WhereGσ is a Gaussian filter with standard deviation σ and * is the convolution operator. We will
call the result of applying these operators to the image: s00, s10, s01, s20, s11 and s02, where the subscript
denotes the order of the derivatives.
The feature vector calculated for each pixel in the image at scale σ is
λ1= ½ * s20+s02+
𝑞
𝑠20−𝑠02 2+4𝑠11
(6.2)
λ2= ½ * s20+s02−
𝑞
𝑠20−𝑠02 2+4𝑠11
(6.3)
s00, s10 + s01, λ1, λ2, where s10 + s01 is the gradient magnitude and λ1 y λ2 are the eigen values of the
Hessian matrix calculated as follows:
This procedure is repeated with various scales σ0,...,σn−1, and since there are 4 features for each scale, we
obtain a feature vector of size 4n. In our experiments we use n = 4 scales, therefore, we obtain a feature
vector with 16 dimensions.
V. EXPERIMENTATION
We used an image stack obtained from the somatosensory cortex of a rat, with a resolution of
3.686μm per pixel. The thickness of each layer is 20μm. We used 60 images of the stack for training and 10
for testing. The related outcome of the computation is personated in Figure 6.4.
Forthedescriptors that use a Gaussian kernel, we experimentally selected the scales σ0 = 4,σ1 =
5.65,σ2 = 8,σ3 = 11.31,σ4 = 16 for our tests. In our HOG implementation we used 4×4 pixels per cell and
6×6 cells per block. Due to the block division required, we assigned the computed histogram to the central
pixel in each cell.
For the histogram and simple window descriptors, we tested with several window and bin sizes. A 20 ×
20 pixel window with 10 bins for the histogram and a 15×15 box for the simple window had the best
performance.
For the LBP we use the radius of 10 pixels with 25 sample points from which we obtain the LBP
code for each pixel. With such codification we obtain a new image stack on which we compute a 10 bins
histogram on a 20×20 pixels window, from which we build our feature vector.
In the first row the best results with Random Forest Classifier and in these cond row the best results with
Gaussian Classifier For classification purposes we used two algorithms. A Gaussian and a Random Forest
Classifier.
5. Gaussian Multi-Scale Feature Disassociation Screening In Tuberculosis
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The Gaussian classifier is a generative parametric classifier that assumes Gaussian class conditional
distributions. We chose this classifier because of its simplicity and speed. Random forests operate by
training a multitude of decision trees and selecting the class label that is the mode of the classes output by
individual trees. We used the scikit-learn [PVG et.al.,-11]implementation of the Random Forest classifier
with 100 decision trees.
ThehighdimensionalityofthefeaturevectorsproducedbytheHOGandRadon-
Likefeaturedescriptors,giventhe numberoftrainingsamples,
producedgenerateGaussiandistributionswithsingularcovariancematrices.Forthisreasoninourexperimentswedo
notuseaGaussianclassifierwithHOGandRadon-
Likefeatures.Similarly,wedonotshowtheresultsoftheRandomForestclassifierwithLBPfeatures,givenitspoorper
formance.
In our experiments we use the ROC curve of each class against there stand the Jaccard Index as
comparison indices. We have performed an extensive set of experiments involving different feature
configuration. We compared our work with the Radon-Like features [KVP-10] and the work in [LSA et.al.,-
12] but testing the descriptors of their feature vector individually, i.e., the histogram and Ray descriptors were
tested separately. The results of our experiments show that the Random Forest classifier achieves the best
performance for both mitochondria and synapse segmentation. However, as shown in Table1.0, the time it
takes to train and classify is roughly one order of magnitude larger than the Gaussian classifier. On the other
hand, the Gaussian Classifier is significantly faster than the Random Forest at expense of a margin al loss in
performance.
The GRIMS descriptors provide the best performance for mitochondria segmentation, closely
followed by the simple window. On the other hand, for the segmentation of synapses, the simple window
descriptor provides the best performance, immediately a head of the GRIMS.
Random Forest Classifier
Descriptor Learning Prediction
LBP 27.0 min 36.0s
Simple Window 126.1min 29.4s
Histogram 12.3min 13.29s
GRIMS 35.2min 15.54s
Ray 53.1min 19.0s
HOG 54.4min 21.0s
Laplacian of Gaussian 18.4min 11.3s
Difference of Gaussians 18.6min 11.9s
Gaussian Classifier
Descriptor Learning Prediction
LBP 1.4s 1.3s
Simple Window 3.0s 1.2s
Histogram 3.0s 0.5s
GRIMS 1.2s 1.0s
Ray 2.0s 0.4s
Eigen values of Structure Tensor 2.1s 0.2s
Laplacian of Gaussian 2.3s 1.0s
Difference of Gaussians 2.3s 1.1s
Table 1.0: Learning and Prediction in Classifiers
VI. SUMMARY
Edge detection, especially step edge detection has been widely applied in various different computer
vision systems, which is an important technique to extract useful structural information from different vision
objects and dramatically reduce the amount of data to be processed. It was found that, the requirements for the
application of edge detectionon diverse vision systems are relatively the same.
We tested nine feature descriptors with two classifiers in an EM stack for the joint segmentation of
mitochondria and synapses. Our results show that GRIMS and simple window descriptors exhibit the best
performance. Although the Random Forest classifier achieves better precision, we suggest the use of the
Gaussian Classifier given the large size of the typical EM image stacks and the gains in speed provided by this
classifier.
6. Gaussian Multi-Scale Feature Disassociation Screening In Tuberculosis
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Thus anautomated system for CXRS manifestations of TB screens.The system is currently set for
practical use,where they are part of a mobile system for TB screening in remote areas. When the CXR as input,
our system first segments the lung area with an optimization method based on Canny edge detection. This
method combines intensity information with personalized lungs At las models derived from the training set. We
calculate a set of shape, edge and texture features as input a binary classifier, then classifies the input image in to
a predetermined either normal or abnormal.
REFERENCES
[1]. [BMR et.al.,-13]L. Blazquez-Llorca, A. Merchan-Perez, J. R. Rodrıguez, J. Gascon, and J. DeFelipe, “FIB/SEM technology and
alzheimer’s disease: Three-dimensional analysis of human cortical synapses,” Journal of Alzheimer’s Disease, vol. 34, no. 4, pp.
995–1013, 2013.
[2]. [Bol-79]Bolles, Robert; Learning Theory ; Holt , Rinchart and Winston, New York 1979
[3]. [Cha-02] M. S. Charikar. “Similarity Estimation Techniques from Rounding Algorithms”. In Proceedings of the 34th Annual
ACM Symposium on Theory of Computing, 2002.
[4]. [CJP et.al.,12] S. Candemir, S. Jaeger, K. Palaniappan, S. Antani, and G. Thoma, “Graph-cut based automatic lung boundary
detection in chest radiographs,” in Proc. IEEE Healthcare Technol. Conf.: Translate. Eng. Health Med., 2012, pp. 31–34.
[5]. [CNL-10] D.-H. Cho, T. Nakamura, and S. Lipton, “Mitochondrial dynamics in cell death and neurodegeneration,” Cellular and
Molecular Life Sciences, vol. 67, no. 20, pp. 3435–3447, 2010.
[6]. [CPA-13] S. Candemir, K. Palaniappan, and Y. Akgul, “Multi-class regularization parameter learning for graph cut image
segmentation,” in International Symposium on Biomedical Imaging, 2013, pp. 1473–1476.
[7]. [CS-00] Cristianini, N. and Shawe-Taylor, J. (2000). An introduction to Support Vector Machines and other kernel-based
learning methods. Cambridge University Press, Cambridge, UK.
[8]. [Del-10] J. DeFelipe, “From the connectome to the synaptome: An epic love story,” Science, vol. 330, no. 6008, pp. 1198–
1201, 2010.
[9]. [DH-04] W. Denk and H. Horstmann, “Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue
nanostructure,” PLoS Biol, vol. 2, no. 11, p. e329, 10 2004.
[10]. [GME-12] R. Giuly, M. Martone, and M. Ellisman, “Method: automatic segmentation of mitochondria utilizing patch
classification, contour pair classification, and automatically seeded level sets,” BMC Bioinformatics, vol. 13, no. 1, p. 29, 2012.
[11]. [GTC-11] Global Tuberculosis Control 2011. World Health Organization, 2011.
[12]. [JKA et.al.,-12] S. Jaeger, A. Karargyris, S. Antani, and G. Thoma, “Detecting tuberculosis in radiographs using combined lung
masks,” in Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBS), 2012, pp. 4978– 4981.
[13]. [KL-10] N. Kasthuri and J. W. Lichtman, “Neurocartography,” Neuropsy-chopharmacology, 2010.
[14]. [KMWet.al.,-08] G. Knott, H. Marchman, D. Wall, and B. Lich, “Serial section scanning electron microscopy of adult brain
tissue using focused ion beam milling,” Journal of Neuroscience, vol. 28, no. 12, pp. 2959–2964, 2008.
[15]. [KSS et.al.,-11] A. Kreshuk, C. N. Straehle, C. Sommer, U. Koethe, M. Cantoni, G. Knott, and F. A. Hamprecht, “Automated
detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images,” PLoS ONE, vol. 6, no.
10, p. e24899, 10 2011.
[16]. [LLH et.al.,-07] D. Lee, K.-H. Lee, W.-K. Ho, and S.-H. Lee, “Target cell-specific involvement of presynaptic mitochondria in
post-tetanic potentiation at hippocampal mossy fiber synapses,” The Journal of Neuroscience, 2007.
[17]. [LSA et.al.,-12] A. Lucchi, K. Smith, R. Achanta, G. Knott, and P. Fua, “Supervoxel-based segmentation of mitochondria in em
image stacks with learned shape features,” IEEE Transactions on Medical Imaging, vol. 31, no. 2, pp. 474–486, 2012.
[18]. [OPH-96] T. Ojala, M. Pietikainen, and D. Harwood, “A comparative study of texture measures with classification based on
featured distributions,” Pattern recognition, vol. 29, no. 1, pp. 51–59, 1996.
[19]. [Pat-72] E.A. Patrick, Fundamentals of Pattern Recognition, Prentice Hall, Inc, N.J.,1972.
[20]. [PVG et.al.,-11] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R.
Weiss, V. Dubourg, J. Vander-plas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duch-esnay, “Scikit-learn:
Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[21]. [SCL-09] K. Smith, A. Carleton, and V. Lepetit, “Fast ray features for learning irregular shapes,” in Proc. Int. Conference on
Computer Vision, 2009, pp. 397–404.
[22]. [VZ-03] M. Varma and A. Zisserman, “Texture classification: Are filter banks necessary?” in IEEE Conference on Computer
Vision and Pattern Recognition, vol. 2, 2003, pp. 691–698.
[23]. [WHO-11] Stop TB Partnership, The Global Plan to Stop TB 2011-2015. World Health Organization, 2011.
[24]. [WHO-12] WHO, Global Tuberculosis Report. World Health Organization, 2012.