Multi Scale Directional Filtering Based Method for Follicular Lymphoma Grading
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IRJET- Analysis of Skin Cancer using ABCD Technique
Segmentation and Classification of Skin Lesions Based on Texture Features
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Cervical Spine Range of Motion Measurement Utilizing Image Analysis - VISAPP2022
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Case Conference on the 26th Generalist Training Seminar
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This document provides information on immobilizing giraffes including:
- Recommended drug combinations for giraffe immobilization including etorphine or carfentanil combined with xylazine, or thiafentanil combined with medetomidine and ketamine.
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Roschewski-Mark-IV-hematology_forum_2016
This document discusses modern treatment strategies for diffuse large B-cell lymphoma (DLBCL). It summarizes that DLBCL is molecularly distinct diseases with different biological characteristics and outcomes. Targeted therapies like ibrutinib and lenalidomide show promise for the activated B-cell (ABC) subtype of DLBCL. Monitoring circulating tumor DNA is presented as a very promising tool for early detection of treatment failure or recurrence, which can allow for pre-emptive treatment changes or early intervention.
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TTP HUSについて
Microangiopathic Hemolytic Anemia refers to thrombotic microangiopathy which involves blood clots in small blood vessels along with hemolysis and symptoms of clotting. There are three types - thrombotic thrombocytopenic purpura (TTP), hemolytic uremic syndrome (HUS), and disseminated intravascular coagulation (DIC). TTP and HUS specifically present with microangiopathic hemolytic anemia, thrombocytopenia, and symptoms of clotting. TTP typically affects the central nervous system systemically while HUS is limited to the kidneys. The cause of TTP is often a genetic deficiency in the enzyme ADAMTS13
Lymphomas 5
This document discusses techniques used to study lymphomas, including immunophenotyping, cytogenetics, and molecular analysis. Immunophenotyping helps differentiate between benign and malignant processes and between B and T cell neoplasms by identifying cell surface markers. Cytogenetics identifies translocations and deletions that help classify lymphomas. Molecular analysis identifies immunoglobulin and T-cell receptor gene rearrangements in B and T cell malignancies, respectively. The document also summarizes the history of lymphoma classification systems and the current WHO system, which classifies lymphomas based on morphology, immunophenotype, molecular abnormalities, and clinical profile.
Day case anesthesia
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Molecular pathology of lymphoma by dr ramesh
Lymphoma classification is based on the cell of origin (B-cell, T-cell, NK-cell). Molecular biology techniques including immunophenotyping and genetic studies are important for accurate diagnosis and classification. Immunophenotyping uses cell surface markers identified by clusters of differentiation (CD markers) to determine the cell lineage and stage of differentiation. Common CD markers for B-cells, T-cells, myeloid cells and other lineages are described. Molecular studies can identify genetic abnormalities that help classify specific lymphoma subtypes. Together these molecular techniques provide crucial information beyond what is visible by microscopy alone.
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This document discusses thrombotic thrombocytopenic purpura (TTP). It begins by presenting a case of a 32-year-old woman presenting with headaches, difficulty speaking and moving her tongue, and numbness. Her exam and labs show thrombocytopenia and schistocytes. The document then discusses the differential diagnosis, epidemiology, terminology, definitions, types, presentations, investigations, and treatment of TTP, with a focus on plasma exchange therapy to remove antibodies and replace deficient ADAMTS13 protease.
Thrombotic Thrombocytopenic Purpura / Hemolytic Uremic Syndrome (Questions & ...
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- A new version of this lecture is available at: https://www.slideshare.net/MohammedGawad/thrombotic-microangiopathy-tma-in-adults-and-acute-kidney-injury-dr-gawad
- Visit our website for more lectures: www.NephroTube.com
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- Follow us on twitter: www.twitter.com/NephroTubeLymphoma
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Ttp in immediate postpartum
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This document discusses a hybrid machine learning approach for classifying liver cancer in ultrasound images using MATLAB. It begins with an introduction to image processing and its purposes. Next, it discusses the existing methodology which uses GLCM and NHSVM for feature extraction and classification. The proposed methodology improves upon this by using a hybrid of GLCM, LBP, SVM and KNN for feature extraction and classification. It then describes the various modules involved - input image, preprocessing, segmentation, feature extraction, classification and performance estimation. Finally, it provides a tentative schedule and references. The goal is to improve classification performance by utilizing a hybrid machine learning approach.
Detection of exudates draft
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.
CANCER CLUMPS DETECTION USING IMAGE PROCESSING BASED ON CELL COUNTING
This document describes a proposed method for detecting cancer clumps using image processing techniques including cell counting. The method involves preprocessing images using techniques like grayscaling, binarization, and edge detection. Cancer cells are then identified and segmented. Features are extracted from the segmented regions and fed into a deep learning model for classification and counting of cancer cells. The proposed approach aims to automatically detect cancer cells in images as a way to help speed up cancer research and improve accuracy over existing methods. If successfully implemented and refined with feedback, it could open new avenues for cancer cell detection in medical imaging.
Detection of exudates draft
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.
Melanoma Image Segmentation using Self Organized Features Maps
This ppt presents a SOFM (Self Organizing Feature Maps) model addressing the problem of segmentation of Dermoscopic skin cancer images. It proposes a unique way of passing information from the image to the network and shows how to interpret the output of the network. The main aim is to train the network so that it segments novel images correctly.
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...
AAPM2023_SU-300-IePD-F6-4
Purpose: Traditional methods of evaluating cardiotoxicity rely on cardiac radiation doses and do not incorporate functional imaging. Cardiac functional imaging can improve the ability to provide early prediction for clinical outcomes for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained for staging and disease assessment after treatment. Although FDG PET/CT scans are typically used to evaluate the tumor, studies have shown that the PET cardiac signal is predictive of clinical outcomes. Our study aimed to develop novel functional cardiac delta radiomics using pre and post-treatment FDG PET/CT scans to predict for overall survival (OS).
Methods: We conducted a study of 109 lung cancer patients who underwent standard FDG-PET/CT scans pre- and post-radiotherapy. Data from ACRIN 6668 (N=70) and an investigator-initiated lung cancer trial (N=39) for functional avoidance radiotherapy were used. The heart was delineated, and 200 cardiac CT and PET functional radiomics features were selected. Delta radiomics was calculated as the change between pre- and post-PET/CT. The data were divided into 80%/20% training/test set, and feature reduction was performed using Wilcoxon test, hierarchical clustering, and recursive feature elimination. A Gradient Boosting Classifier machine learning model evaluated the ability of the delta PET/CT cardiac radiomics to predict for OS using 10-fold cross-validation for training and area-under-the-curve (AUC) for model assessment.
Results: Median survival was 431 days (range 144 to 1640 days). 4 clinically relevant delta features were identified: pre-CT_Maximum, post-CT_Minimum, delta-CT_GLRM_Run_Variance, delta-PET_GLRM_Run_Entropy. The model showed an AUC of 0.91 on the training set and an AUC of 0.87 on the test set.
Conclusion: This is the first study to evaluate functional cardiac delta radiomic features from standard PET/CT scans with data showing good predictive AUC for OS. If validated, this work provides automated methods to provide functional cardiac information for clinical outcome prediction in lung cancer patients.
LIVE DEMONSTRATION: Understanding the Complimentary Nature of BLI, Ultrasound...
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This will be a full day of live imaging where we will be examining the complimentary nature of three preclinical imaging modalities – bioluminescence, high-frequency ultrasound, and MRI. We will image the same tumor models on each of the imaging modalities; but will also have a normal mouse available to look at cardiovascular function using ultrasound, and brain anatomy using MRI.Introduction to image processing
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Optimal fuzzy rule based pulmonary nodule detection
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Digital radiography (2013)
Digital radiography provides several advantages over film radiography including improved archiving and distribution of images, higher patient throughput, and potential reduction of patient radiation dose. There are two main types of digital radiography - computed radiography which uses imaging plates and direct radiography which involves intrinsic readout processes without cassettes. Direct radiography can be via direct or indirect conversion and uses flat panel detectors incorporating thin film transistors. Digital images allow various processing techniques and can be stored and shared electronically in picture archiving systems.
Microcalcification Enhancement in Digital Mammogram
The document discusses early detection of breast cancer through computer-aided detection of microcalcifications in digital mammograms. It describes microcalcifications and how mammography is used to detect them as early signs of cancer. The problem is the difficulty for radiologists to accurately detect microcalcifications. The goal is to develop a computer model to better detect microcalcification clusters and determine cancer likelihood from mammogram images.
Identik Problem
This document presents a study that uses pre-trained convolutional neural networks (CNNs) as feature extractors for blur detection in digital breast tomosynthesis (DBT) images. Specifically, it examines ResNet18, ResNet50, AlexNet, VGG16 and InceptionV3 CNNs connected to a support vector machine (SVM) classifier to label DBT images as blurry or not blurry. The CNN-SVM combinations are evaluated based on accuracy, receiver-operating characteristic curves, area under the curve, and execution time. The results found that InceptionV3 achieved the best accuracy of 0.9961 and area under the curve of 0.9961, while AlexNet had the shortest processing time. The study aims to
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import pygame
pygame.init() #initializes the Pygame
from pygame.locals import* #import all modules from Pygame
screen = pygame.display.set_mode((798,600))
#changing title of the game window
pygame.display.set_caption('Racing Beast')
#changing the logo
logo = pygame.image.load('car game/logo.jpeg')
pygame.display.set_icon(logo)
run = True
while run:
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
if event.type == pygame.KEYDOWN:
if event.key == K_RIGHT:
maincarX += 5
if event.key == K_LEFT:
maincarX -= 5
#CHANGING COLOR WITH RGB VALUE, RGB = RED, GREEN, BLUE
screen.fill((255,0,0))
Noninvasive diabetes mellitus detection
This document proposes a noninvasive method to detect diabetes mellitus using facial images. It extracts texture features using a Gabor filter bank and color features in the Lab color space from divided facial blocks. These features are classified using k-NN and SVM algorithms. Experimental results show classification accuracies up to 94.28% for k-NN and 97.14% for SVM, providing an alternative to invasive blood glucose tests. The method aims to detect differences in facial skin associated with diabetes status based on traditional Chinese medicine hypotheses.
Radiomics and Deep Learning for Lung Cancer Screening
The document summarizes research on using radiomics and deep learning approaches for lung cancer screening. It describes:
1) Using radiomic features like shape, texture, and intensity from lung nodules on CT scans and an SVM-LASSO model to classify nodules with 87.9% sensitivity and 78.2% specificity, outperforming the Lung-RADS system.
2) A deep learning model developed for a Kaggle competition that achieved 67.4% accuracy on nodule classification but only ranked 99th due to overfitting issues without enough data.
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reliablepaper.pdf
This document proposes a computer-aided lung cancer classification system using curvelet features and an ensemble classifier. It first pre-processes CT images using adaptive histogram equalization to improve contrast. Then it segments the images using kernelized fuzzy c-means clustering. Curvelet features are extracted from the segmented regions and an ensemble classifier is applied to classify regions as benign or malignant. The proposed approach achieves reliable and accurate classification results compared to existing methods, with better performance metrics like accuracy, sensitivity and specificity.
Contourlet Transform Based Method For Medical Image Denoising
Noise is an important factor of the medical image quality, because the high noise of medical imaging will not give us the useful information of the medical diagnosis. Basically, medical diagnosis is based on normal or abnormal information provided diagnose conclusion. In this paper, we proposed a denoising algorithm based on Contourlet transform for medical images. Contourlet transform is an extension of the wavelet transform in two dimensions using the multiscale and directional filter banks. The Contourlet transform has the advantages of multiscale and time-frequency-localization properties of wavelets, but also provides a high degree of directionality. For verifying the denoising performance of the Contourlet transform, two kinds of noise are added into our samples; Gaussian noise and speckle noise. Soft thresholding value for the Contourlet coefficients of noisy image is computed. Finally, the experimental results of proposed algorithm are compared with the results of wavelet transform. We found that the proposed algorithm has achieved acceptable results compared with those achieved by wavelet transform.
Detection of Cysts in Ultrasonic Images of Ovary
Detection of Cysts in Ultrasonic Images of OvaryInternational Journal of Science and Research (IJSR)
In this paper, cysts are detected in the ultrasonic images of ovary. PCOS is an endocrine disorder affecting women of reproductive age. This syndrome is mainly seen in women whose age is in between 25 and 35. We are proposing methods for identifying whether a person is suffering from Polycystic Ovary Syndrome (PCOS) or not. Ultrasound imaging of the follicles gives important information about the size, number and mode of arrangement of follicles, position and response to hormonal stimulation. A thresholding function is applied for denoising the image in the wavelet domain. Before the segmentation process the ultrasonic image is preprocessed using contrast enhancement technique. Morphological approach is used for implementing contrast enhancement. This is performed in order to improve the clarity and quality of the image. Fuzzy c-means clustering algorithm is applied to the resultant image. Finally the cysts are detected with the help of clusters. The efficiency of the algorithm depends upon the value of Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
Computer aided detection of pulmonary nodules using genetic programming
This document describes a method for detecting pulmonary nodules in CT scans using genetic programming. It first segments the lung regions from CT images and extracts nodule candidates. Features are then extracted from the candidates. Genetic programming is used to classify candidates as nodules or non-nodules by optimizing combinations of features. The method was tested on a publicly available lung image database, achieving a true positive rate of over 90% and low false positive rate.
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CANCER CLUMPS DETECTION USING IMAGE PROCESSING BASED ON CELL COUNTING
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Melanoma Image Segmentation using Self Organized Features Maps
Melanoma Image Segmentation using Self Organized Features Maps
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...
LIVE DEMONSTRATION: Understanding the Complimentary Nature of BLI, Ultrasound...
LIVE DEMONSTRATION: Understanding the Complimentary Nature of BLI, Ultrasound...
Optimal fuzzy rule based pulmonary nodule detection
Optimal fuzzy rule based pulmonary nodule detection
Microcalcification Enhancement in Digital Mammogram
Microcalcification Enhancement in Digital Mammogram
Radiomics and Deep Learning for Lung Cancer Screening
Radiomics and Deep Learning for Lung Cancer Screening
Contourlet Transform Based Method For Medical Image Denoising
Contourlet Transform Based Method For Medical Image Denoising
Computer aided detection of pulmonary nodules using genetic programming
Computer aided detection of pulmonary nodules using genetic programming
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TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by...
TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by Stamler, Verified Chapters 1 - 33, Complete Newest Version Community Health Nursing A Canadian Perspective, 5th Edition by Stamler, Verified Chapters 1 - 33, Complete Newest Version Community Health Nursing A Canadian Perspective, 5th Edition by Stamler Community Health Nursing A Canadian Perspective, 5th Edition TEST BANK by Stamler Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Pdf Chapters Download Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Pdf Download Stuvia Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Study Guide Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Ebook Download Stuvia Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Questions and Answers Quizlet Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Studocu Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Quizlet Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Pdf Chapters Download Community Health Nursing A Canadian Perspective, 5th Edition Pdf Download Course Hero Community Health Nursing A Canadian Perspective, 5th Edition Answers Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Ebook Download Course hero Community Health Nursing A Canadian Perspective, 5th Edition Questions and Answers Community Health Nursing A Canadian Perspective, 5th Edition Studocu Community Health Nursing A Canadian Perspective, 5th Edition Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Pdf Chapters Download Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Pdf Download Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Study Guide Questions and Answers Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Ebook Download Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Questions Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Studocu Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Stuvia
Best Ayurvedic medicine for Gas and Indigestion
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
The Electrocardiogram - Physiologic Principles
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Osteoporosis - Definition , Evaluation and Management .pdf
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
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Multi Scale Directional Filtering Based Method for Follicular Lymphoma Grading
- 1. Multi-scale directional filtering based method for Follicular Lymphoma grading ALİCAN BOZKURT, A. ENIS CETIN MUSCLE WORKSHOP, ANTALYA 03.10.2013
- 2. Follicular Lymphoma grading • Follicular Lymphoma (FL) • Presence of a follicular or nodular pattern of growth presented by follicle center B cells • centrocytes and centroblasts. Grade 1 (0-5) Grade 2 (6-15) Grade 3 (>15) 2
- 3. Follicular Lymphoma grading Grade 1 Grade 2 Grade 3 3
- 4. Follicular Lymphoma grading • Pioneer work by Sertel et al: • mimicked the manual approach of pathologists, i.e., identifying the number of centroblasts in the sample. Based on this, a decision on the grade of the sample can be made. • Accuracy for CB detection was about 80%. Sertel, Olcay, et al. "Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading." Journal of Signal Processing Systems 55.1-3 (2009): 169-183. 4
- 5. Follicular Lymphoma grading • Improvement by Suhre • Hp and Ep denote the projections on the H and E vectors proposed by Cosatto et al. (2008) to model Hematoxylin and Eosin (H&E) staining. • Grades (1,2) and 3 can be distinguished by comparing the histograms via Kullback-Leibler (KL) divergence. • For differentiating grades 1 and 2, we choose a Bayesian classifier. (DCT of the eigenvalue histograms) The underlying PDF is assumed to be sparse, therefore only q coefficients are used. Grade 1 Grade 2 Grade 3 98.89 98.89 100 5
- 6. Follicular Lymphoma grading • Our Work • • • • Approaches the problem as texture recognition program Based on a novel multi-scale feature extraction method LDA SVM 6
- 7. Directional filtering •Main idea: rotating a 1D filter along desired orientation •Easy for θ=k x 45°, k=0,1,2,… •Not easy for θ≠k x 45° • Bilinear/cubic interpolation • Our method: coefficients proportional to length of line segments enclosed by pixels • Also used in CT Herman, Gabor T. "Image reconstruction from projections." Image Reconstruction from Projections: Implementation and Applications 1 (1979). 7
- 8. 8
- 12. 12
- 14. Feature extraction Step 0 • Input Image 14
- 15. Feature extraction • Input Image Step 0 Step 1 • Convert Image to gray level 15
- 16. Feature extraction Step 0 μ1 : σ1 : 0,082091 0,084891 0,060045 0,080689 0,085836 0,060873 0,14791 0,15201 0,11201 0,14617 0,15402 0,11424 50 50 50 50 100 100 100 100 100 150 150 150 150 150 150 200 200 200 200 200 200 250 250 250 250 250 250 300 300 50 100 300 μ2: σ2: 300 50 100 150 200 250 300 350 400 450 500 300 300 50 50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450 100 150 200 250 300 350 400 450 500 500 50 50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450 0,22597 0,24064 0,11976 0,23731 0,24072 0,36203 0,35692 0,17401 0,37765 0,34842 500 0,12753 Step 2 • Convert Image to gray level • Extract Features 0,19024 20 20 40 40 60 60 60 60 60 60 40 40 40 40 20 20 20 20 80 80 80 80 100 100 100 100 100 120 120 120 120 80 80 1 00 1 20 140 1 60 50 10 0 150 200 250 140 160 50 100 150 200 250 100 150 2 00 140 160 50 50 120 140 160 140 160 1 40 μ3: σ3: Step 1 • Input Image 100 150 200 250 250 160 50 100 150 200 25 0 50 100 150 200 250 0,49943 0,54883 0,35954 0,55623 0,56736 0,30949 0,6949 0,46078 0,72141 0,68851 0,39779 0,65361 Φ = [μ1 σ1 μ2 σ2 μ3 σ3] (1x36 feature vector) 16
- 18. Dataset Same dataset used by Suhre 90 images per grade Grade 1 Grade 2 Grade 3 18
- 19. Background 19
- 20. Results Follicular Lymphoma •Max: 100.00 (Dir. Fil.) •SoA: 99.26 [20] A. Suhre, Novel Methods for Microscopic Image Processing, Analysis, Classification and Compression. PhD thesis, Bilkent University, 2013. 20
- 21. Results 6 Grade 1 Grade 2 Grade 3 4 Feature 2 2 0 -2 -4 -6 -10 -8 -6 -4 -2 Feature 1 0 2 4 6 21
- 22. Background 22
- 23. Results 23
- 24. Conclusion •New directional filter construction and multiscale filtering framework • Computationally efficient (2x faster than the closest competitor) •Follicular Lymphoma Grading as an application of the framework • Mean and standard deviation of directional filter outputs as features • LDA as feature reduction (to 2D) • SVM as classifier • Outperformed state of art 24
- 25. Thank You! 25
Editor's Notes
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