The optic disc (OD) is one of the important part of the eye for detecting various diseases such as Diabetic Retinopathy and Glaucoma. The localization of optic disc is extremely important for determining hard exudates and lesions. Diagnosis of the disease can prevent people from vision loss. This paper analyzes various techniques which are proposed by different authors for the exact localization of optic disc to prevent vision loss.
Retinal Macular Edema Detection Using Optical Coherence Tomography ImagesIOSRJVSP
Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed on the OCT scan and the thickness between the two layers in the different regions are determined. Area enclosed between the two layers is also estimated. Support Vector Machine, a binary classifier is used to draw a classification between normal and abnormal OCT scans. Region-wise thickness, a few Haralick’s features, area between ILM and RPE and a few wavelet features are used to train the classifier. The classifier yielded an accuracy of 95% and a sensitivity of 100%. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
MELANOMA CELL DETECTION IN LYMPH NODES HISTOPATHOLOGICAL IMAGES USING DEEP LE...sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.
— Treatment of nasopharyngeal carcinoma is done by advanced radiotherapy techniques like VMAT (Volumetric Modulated Arc Therapy) where dose to critical organs around tumour is of concern. Present study aimed to describe radiation dose to critical organs in nasopharyngeal cancer patients using VMAT technique. Study was conducted on 10 carcinoma nasopharynx patients treated by VMAT technique at a super-specialty cancer institute in Rajasthan. The structures were contoured using RTOG (Radiation Therapy Oncology Group) guidelines and dose prescription to PTV (Planning Target Volume) was such that 95% iso-dose covered 100% of PTV. Constraints to the OARs (Organs at risk) were as per QUANTEC (Quantitative Analysis of Normal Tissue Effects in the Clinic). VMAT planning was done by double arc using Eclipse (v 10.0.42) treatment planning system. Mean dose to brain stem, spinal cord and optic chiasma were 51.79 Gy, 45.92 Gy and 18.8 Gy respectively. Mean dose to left and right temporal lobes was 22.7Gy and 24.3Gy. Dose to right and left eye were 20.6 Gy and 19.2 Gy while dose to right and left lenses were 5.9Gy and 5.8 Gy respectively. Dose to brain stem, spinal cord, optic chiasma, eyes, lens and temporal lobes were below the dose constraints. VMAT is an effective way to deliver maximum radiation to tumour tissue while providing better sparing of normal tissue and less doses to OARs in carcinoma nasopharynx.
New Noise Reduction Technique for Medical Ultrasound Imaging using Gabor Filt...CSCJournals
Ultrasound (US) imaging is an important medical diagnostic method, as it allows the examination of several internal body organs. However, its usefulness is diminished by signal dependent noise known as speckle noise. Speckle noise degrades target detectability in ultrasound images and reduces contrast and resolution, affecting the ability to identify normal and pathological tissue. For accurate diagnosis, it is important to remove this noise from ultrasound images. In this study, a new filtering technique is proposed for removing speckle noise from medical ultrasound images. It is based on Gabor filtering. Specifically, a preprocessing step is added before applying the Gabor filter. The proposed technique is applied to various ultrasound images, and certain measurement indexes are calculated, such as signal to noise ratio, peak signal to noise ratio, structure similarity index, and root mean square error, which are used for comparison. In particular, five widely used image enhancement techniques were applied to three types of ultrasound images (kidney, abdomen and ortho). The main objective of image enhancement is to obtain a highly detailed image, and in that respect, the proposed technique proved superior to other widely used filters.
FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR sipij
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of thedisease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which isthe ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with
thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated.
The validity of this new method has been tested on 365 colour fundus images from two different publicly
available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method
seems to be promising and useful for clinical work.
Identification of Focal Cortical Dysplasia (FCD) can be difficult due to the subtle MRI changes. Though sequences like FLAIR (fluid attenuated inversion recovery) can detect a large majority of these lesions, there are smaller lesions without signal changes that can easily go unnoticed by the naked eye. The aim of this study is to improve the visibility of Focal Cortical Dysplasia lesions in the T1 weighted brain MRI images. In the proposed method, we used a complex diffusion based approach for calculating the FCD affected areas.
The optic disc (OD) is one of the important part of the eye for detecting various diseases such as Diabetic Retinopathy and Glaucoma. The localization of optic disc is extremely important for determining hard exudates and lesions. Diagnosis of the disease can prevent people from vision loss. This paper analyzes various techniques which are proposed by different authors for the exact localization of optic disc to prevent vision loss.
Retinal Macular Edema Detection Using Optical Coherence Tomography ImagesIOSRJVSP
Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed on the OCT scan and the thickness between the two layers in the different regions are determined. Area enclosed between the two layers is also estimated. Support Vector Machine, a binary classifier is used to draw a classification between normal and abnormal OCT scans. Region-wise thickness, a few Haralick’s features, area between ILM and RPE and a few wavelet features are used to train the classifier. The classifier yielded an accuracy of 95% and a sensitivity of 100%. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
MELANOMA CELL DETECTION IN LYMPH NODES HISTOPATHOLOGICAL IMAGES USING DEEP LE...sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.
— Treatment of nasopharyngeal carcinoma is done by advanced radiotherapy techniques like VMAT (Volumetric Modulated Arc Therapy) where dose to critical organs around tumour is of concern. Present study aimed to describe radiation dose to critical organs in nasopharyngeal cancer patients using VMAT technique. Study was conducted on 10 carcinoma nasopharynx patients treated by VMAT technique at a super-specialty cancer institute in Rajasthan. The structures were contoured using RTOG (Radiation Therapy Oncology Group) guidelines and dose prescription to PTV (Planning Target Volume) was such that 95% iso-dose covered 100% of PTV. Constraints to the OARs (Organs at risk) were as per QUANTEC (Quantitative Analysis of Normal Tissue Effects in the Clinic). VMAT planning was done by double arc using Eclipse (v 10.0.42) treatment planning system. Mean dose to brain stem, spinal cord and optic chiasma were 51.79 Gy, 45.92 Gy and 18.8 Gy respectively. Mean dose to left and right temporal lobes was 22.7Gy and 24.3Gy. Dose to right and left eye were 20.6 Gy and 19.2 Gy while dose to right and left lenses were 5.9Gy and 5.8 Gy respectively. Dose to brain stem, spinal cord, optic chiasma, eyes, lens and temporal lobes were below the dose constraints. VMAT is an effective way to deliver maximum radiation to tumour tissue while providing better sparing of normal tissue and less doses to OARs in carcinoma nasopharynx.
New Noise Reduction Technique for Medical Ultrasound Imaging using Gabor Filt...CSCJournals
Ultrasound (US) imaging is an important medical diagnostic method, as it allows the examination of several internal body organs. However, its usefulness is diminished by signal dependent noise known as speckle noise. Speckle noise degrades target detectability in ultrasound images and reduces contrast and resolution, affecting the ability to identify normal and pathological tissue. For accurate diagnosis, it is important to remove this noise from ultrasound images. In this study, a new filtering technique is proposed for removing speckle noise from medical ultrasound images. It is based on Gabor filtering. Specifically, a preprocessing step is added before applying the Gabor filter. The proposed technique is applied to various ultrasound images, and certain measurement indexes are calculated, such as signal to noise ratio, peak signal to noise ratio, structure similarity index, and root mean square error, which are used for comparison. In particular, five widely used image enhancement techniques were applied to three types of ultrasound images (kidney, abdomen and ortho). The main objective of image enhancement is to obtain a highly detailed image, and in that respect, the proposed technique proved superior to other widely used filters.
FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR sipij
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of thedisease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which isthe ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with
thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated.
The validity of this new method has been tested on 365 colour fundus images from two different publicly
available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method
seems to be promising and useful for clinical work.
Identification of Focal Cortical Dysplasia (FCD) can be difficult due to the subtle MRI changes. Though sequences like FLAIR (fluid attenuated inversion recovery) can detect a large majority of these lesions, there are smaller lesions without signal changes that can easily go unnoticed by the naked eye. The aim of this study is to improve the visibility of Focal Cortical Dysplasia lesions in the T1 weighted brain MRI images. In the proposed method, we used a complex diffusion based approach for calculating the FCD affected areas.
Detection of Glaucoma using Optic Disk and Incremental Cup Segmentation from ...theijes
Medical researchers, detection of eye disease is very important because it may causes blindness. Glaucoma is one of the diseases that cause blindness. Standard procedure for detection glaucoma is to analysis of optic disk (OD) and cup region in retinal image. In this paper, introduce an automatic OD parameterized technique which is based on segmentation and Incremental Cup segmentation. The incremental cup segmentation method is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is applied to derive a reliable subset of vessel bends called r-bends followed by a local 2-D spline fitting to derive the desired cup boundary. The results are compared with existing methods using different retinal images.
Lipedemia and periodontitis article journal of arab boardWalid Altayeb
Recent studies have proven that periodontal disease can produce numerous changes in systemic health changing the blood chemistry with a rise in proteins and lipids in the serum. These factors explain, at least in part, the probable association between periodontitis and the susceptibility for certain systemic diseases, such as the increased risk of cardiovascular disease. The aim of this study was to evaluate the association between pathologic levels of lipidemia and periodontitis.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
Brain Tumor Area Calculation in CT-scan image using Morphological Operationsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
Glaucoma Disease Diagnosis Using Feed Forward Neural Network ijcisjournal
Glaucoma is an eye disease which damages the optic nerve and or loss of the field of vision which leads to
complete blindness caused by the pressure buildup by the fluid of the eye i.e. the intraocular pressure
(IOP). This optic disorder with a gradual loss of the field of vision leads to progressive and irreversible
blindness, so it should be diagnosed and treated properly at an early stage. In this paper,
thedaubechies(db3) or symlets (sym3)and reverse biorthogonal (rbio3.7) wavelet filters are employed for
obtaining average and energy texture feature which are used to classify glaucoma disease with high
accuracy. The Feed-Forward neural network classifies the glaucoma disease with an accuracy of 96.67%.
In this work, the computational complexity is minimized by reducing the number of filters while retaining
the same accuracy.
Glaucoma is a chronic eye disease in which the optic nerve head is progressively damaged which leads to loss of
vision. Early diagnosis and treatment is the key to preserving sight in people with glaucoma. Current tests using
intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Assessment of the
damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing. This paper
presents superpixel classification based optic disc and optic cup segmentation for glaucoma screening. In optic disc
segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic
cup segmentation, in addition to the histograms and centre surround statistics, the location information is also included
into the feature space to boost the performance. The segmented optic disc and optic cup are used to compute the CDR
for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier
to confirm Glaucoma given patient.
Keywords — IOP measurement, optic cup segmentation, optic disc segmentation, CDR.
Recent diagnostic advances simplified to assist in easy learning with descriptive pictures.Principles of OCT, HRT, CSLO, GDx and interpretation of the same explained with relevant images. The terms ganglion cell complex, glaucoma probabity score and corneal hysteresis explained.
Retinal image analysis using morphological process and clustering techniquesipij
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
Automated histopathological image analysis: a review on ROI extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Feature selection/extraction methods aimed to reduce the Microarray data. Basically in this comparative analysis, we have taken into account different feature selection and extraction strategies used up till now in the field of Biomedical. In the field of pattern recognition and biomedical imaging, dimensionality reduction is the central area of the research. Some mostly used features selection/extraction methods aim to analyze the most efficient data and achieve the stable performance of the algorithms, as well as improve the accuracy and performance of the classifier. This analysis also highlights widely used dimensionality reduction techniques used up till now in the field of biomedical imaging for the purpose to explore their potency, and weak points.
Lec6: Pre-Processing for Nuclear Medicine ImagesUlaş Bağcı
2017 Spring, UCF Medical Image Computing
1. The use of PET/SPECT, PET/CT and MRI/PET Images
2. What to measure from Nuclear Medicine Images?
3. Denoising Nuclear MedicineI mages
4. PartialVolumeCorrection
ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Detection of Glaucoma using Optic Disk and Incremental Cup Segmentation from ...theijes
Medical researchers, detection of eye disease is very important because it may causes blindness. Glaucoma is one of the diseases that cause blindness. Standard procedure for detection glaucoma is to analysis of optic disk (OD) and cup region in retinal image. In this paper, introduce an automatic OD parameterized technique which is based on segmentation and Incremental Cup segmentation. The incremental cup segmentation method is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is applied to derive a reliable subset of vessel bends called r-bends followed by a local 2-D spline fitting to derive the desired cup boundary. The results are compared with existing methods using different retinal images.
Lipedemia and periodontitis article journal of arab boardWalid Altayeb
Recent studies have proven that periodontal disease can produce numerous changes in systemic health changing the blood chemistry with a rise in proteins and lipids in the serum. These factors explain, at least in part, the probable association between periodontitis and the susceptibility for certain systemic diseases, such as the increased risk of cardiovascular disease. The aim of this study was to evaluate the association between pathologic levels of lipidemia and periodontitis.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
Brain Tumor Area Calculation in CT-scan image using Morphological Operationsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
Glaucoma Disease Diagnosis Using Feed Forward Neural Network ijcisjournal
Glaucoma is an eye disease which damages the optic nerve and or loss of the field of vision which leads to
complete blindness caused by the pressure buildup by the fluid of the eye i.e. the intraocular pressure
(IOP). This optic disorder with a gradual loss of the field of vision leads to progressive and irreversible
blindness, so it should be diagnosed and treated properly at an early stage. In this paper,
thedaubechies(db3) or symlets (sym3)and reverse biorthogonal (rbio3.7) wavelet filters are employed for
obtaining average and energy texture feature which are used to classify glaucoma disease with high
accuracy. The Feed-Forward neural network classifies the glaucoma disease with an accuracy of 96.67%.
In this work, the computational complexity is minimized by reducing the number of filters while retaining
the same accuracy.
Glaucoma is a chronic eye disease in which the optic nerve head is progressively damaged which leads to loss of
vision. Early diagnosis and treatment is the key to preserving sight in people with glaucoma. Current tests using
intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Assessment of the
damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing. This paper
presents superpixel classification based optic disc and optic cup segmentation for glaucoma screening. In optic disc
segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic
cup segmentation, in addition to the histograms and centre surround statistics, the location information is also included
into the feature space to boost the performance. The segmented optic disc and optic cup are used to compute the CDR
for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier
to confirm Glaucoma given patient.
Keywords — IOP measurement, optic cup segmentation, optic disc segmentation, CDR.
Recent diagnostic advances simplified to assist in easy learning with descriptive pictures.Principles of OCT, HRT, CSLO, GDx and interpretation of the same explained with relevant images. The terms ganglion cell complex, glaucoma probabity score and corneal hysteresis explained.
Retinal image analysis using morphological process and clustering techniquesipij
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
Automated histopathological image analysis: a review on ROI extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Feature selection/extraction methods aimed to reduce the Microarray data. Basically in this comparative analysis, we have taken into account different feature selection and extraction strategies used up till now in the field of Biomedical. In the field of pattern recognition and biomedical imaging, dimensionality reduction is the central area of the research. Some mostly used features selection/extraction methods aim to analyze the most efficient data and achieve the stable performance of the algorithms, as well as improve the accuracy and performance of the classifier. This analysis also highlights widely used dimensionality reduction techniques used up till now in the field of biomedical imaging for the purpose to explore their potency, and weak points.
Lec6: Pre-Processing for Nuclear Medicine ImagesUlaş Bağcı
2017 Spring, UCF Medical Image Computing
1. The use of PET/SPECT, PET/CT and MRI/PET Images
2. What to measure from Nuclear Medicine Images?
3. Denoising Nuclear MedicineI mages
4. PartialVolumeCorrection
ImageEnhancement • Filtering • Smoothing • Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Automatic System for Detection and Classification of Brain TumorsFatma Sayed Ibrahim
Automatic system for brain tumors detection based on DICOM MRI images
Surveying methodologies of from preprocessing to classifications
Implementing comparative study.
Proposed technique with highest accuracy and lest elapsed time.
Michael DeBrota et al. - Assessment of Computational Histopathology in Thorac...Michael DeBrota
Background and Hypothesis:
Thoracic aortic aneurysm (TAA) histopathology includes elastic fiber (EF) abnormalities, mucoid extracellular matrix (MECM) accumulation, and smooth muscle derangement in the aortic medial layer. While semi-quantitative grading of these characteristics is a standard practice, computational characterization of medial layer components may facilitate novel quantitative analyses at higher throughput. We hypothesized that computational results would correlate with results of semi-quantitative grading of aortic histopathology.
Experimental Design:
Formalin-fixed, paraffin-embedded human aortic tissue sections were stained with Movat’s pentachrome to characterize aortic microstructure. Sections were also immunostained for nitrotyrosine residues to assess oxidative stress. Samples were initially graded semi-quantitatively by two independent blinded readers. Next, computational histopathology software was used a) to quantify the proportions of EF, MECM, and cellular area in the medial layer of pentachrome-stained sections and b) to quantify the distribution and intensity of positive nitrotyrosine staining in immunostained sections. Association between semi-quantitative grading and computed values was tested with ANOVA.
Results:
The cohort included 74 participants who underwent prophylactic aortic replacement for TAA and 23 healthy controls. The mean age was 54±17 years. On average, EFs accounted for 49% (range 6-90%) of medial tissue area, whereas MECM accounted for 25% (1-73%). The overall semi-quantitative grade of medial degeneration severity was associated with decrease in EF fraction (p=0.02). The grade of EF thinning also strongly correlated with decrease in EF fraction (p=1x10-6). Meanwhile, grade for accumulation of MECM was associated with increase in MECM (p=0.004). Increased semi-quantitative grading for nitrotyrosine levels was associated with increased nuclear signal optical density (p=9x10-10) and greater percentage of cells labeled as strongly positive (p=8x10-10).
Conclusion and Potential Impact:
We observed significant correlations between computed quantitative values and semi-quantitative grading. This suggests that computational histopathology is a valid method for investigation of human TAA tissues.
MRI biomarkers for the spinal cord, webinar with Dr. Julien Cohen-Adad.jcohenadad
The video recording is available here: 👉 https://youtu.be/3_xJCSqu5xs
Neuroimaging MRI biomarkers include volumetric measures, microstructure imaging such as diffusion-weighted imaging and magnetization transfer, and functional MRI. These biomarkers nicely complement clinical indices and provide objective means to monitor disease evolution in patients. While being very popular in the brain, MRI biomarkers have been slow to translate to the spinal cord because of the technical difficulties in imaging this organ. In this talk, I will present state-of-the-art solutions for the acquisition and automatic analysis of MRI biomarkers in the spinal cord. During the first part of the talk, I will talk about a recent initiative to standardize acquisition protocol in the spinal cord: the spine-generic project (https://spine-generic.rtfd.io/). During the second part of the talk, we will go through some of the main features of the Spinal Cord Toolbox (SCT, http://spinalcordtoolbox.com/), a popular open-source software package which performs automatic analysis of spinal cord MRI biomarkers.
Finally, we will show example applications of these advanced acquisition and processing methods in various multi-center studies and applied to a variety of diseases: multiple sclerosis, amyotrophic lateral sclerosis, degenerative cervical myelopathy, chronic pain and cancer.
Dr. Cohen-Adad is an Associate Professor at Polytechnique Montreal, Adjunct Professor in the Department of Neurosciences at University of Montreal, Associate Director of the Neuroimaging Functional Unit at the University of Montreal, and Canada Research Chair in Quantitative Magnetic Resonance Imaging. His research focuses on advancing hardware and software MRI methods to help characterizing pathologies in the central nervous system, with a particular focus in the spinal cord. He has published over 130 articles on that topic (https://scholar.google.ca/). Dr. Cohen-Adad also dedicates efforts in bringing the community together by developing open source solutions and by organizing yearly workshops via the www.spinalcordmri.org platform, which he initiated.
Links to publications and work of Dr. Julien Cohen-Adad:
https://pubmed.ncbi.nlm.nih.gov/33039...
https://pubmed.ncbi.nlm.nih.gov/32572...
https://scholar.google.ca/citations?u...
https://spine-generic.rtfd.io/
Histogram-weighted cortical thickness networks for the detection of Alzheimer...Pradeep Redddy Raamana
Presentation delivered by Pradeep Reddy Raamana at 2016 international workshop on Pattern Recognition in Neuroimaging on the topic of histogram-weighted cortical thickness networks for the detection of Alzheimer's disease.
Melanoma Cell Detection in Lymph Nodes Histopathological Images using Deep Le...sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital
histopathological images are typically of very large size, in the order of several billion pixels, automated
identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning
algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the
abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a
Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The
segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine
classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy.
The proposed technique has a low computational complexity.
Articles -Signal & Image Processing: An International Journal (SIPIJ)sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital
histopathological images are typically of very large size, in the order of several billion pixels, automated
identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning
algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the
abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a
Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The
segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine
classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy.
The proposed technique has a low computational complexity.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
17. Mean (COV) and Positivity Threshold* * Positivity threshold calculated from the 100 controls (of 138) with lowest mean cortical amyloid values. Threshold = region mean + 3 SD
38. Group average images AV – negative (n=12) row 1 AV – positive (n=17) row 2 PiB – negative (n=12) row 3 PiB – positive (n=17) row 4
39. Group average surface maps AV – negative row 1 AV – positive row 2 PiB – negative row 3 PiB – positive row 4
40. The relative values of cerebellar gray, pons and white matter are similar for PiB and AV45 (slopes ~1.0 across these structures). Slopes for cortical regions are ~0.60-0.65 (for all reference tissue normalizations)
Subjects plots in order of Subject ID xxx_S_yyyy. Original ADNI Reference of Atlas cereb gray voi
Note the two sections of correlated cortex values with Pons and White Matter. Caused by variability in cerebellar gray values. Likely negative if cortex is markedly lower than pons or white matter. Likely positive if cortex similar to pons, and nearing white matter.
Same for controls, except fewer positive scans. Also white matter values a little higher, hence cortex normalized to white matter will be lower.
Note tighter distribution of alternative ref regions using combined region, Also best distinction of cortex
Threshold defined as mean + 3 std devs of the lowest 100 controls
Point out: 1) tresholds highly dependent on reference , slight dependence on target. 2) Combined more suitable because the amyloid “negative” cortical values have lower variance !
Note effect of lower white matter relative to cerebellum/pons in MCI, causes cortical values in amyloid negative to be slightly higher.
Regions show similar sensitivity except for occipital (least positive)
Note white matter has most subjects positive. Remember that white matter is lower in MCI relative to cerebellum and pons.
Mostly scatter correction effects: which effects image contrast: HRRT old highest, then BioGraph, then GE, then Phlips, then HR+ then HRRT new Effects reduced by white matter or combined normalization
Standardized image scale. If all references regions worked perfectly, then images using different reference regions would be scaled and hence look exactly the same.
Just the n=17
Note negative subjects haven’t changed much, as PVC effects CBL and CTX about the same. In amyloid positive subjects, get considerable enhancement
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