Mojdeh Rastgoo

Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection

This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.

Ensemble approach for differentiation of malignant melanoma

Melanoma is the deadliest type of skin cancer, yet it is the most treatable kind depending on its early diagnosis. The early prognosis of melanoma is a challenging task for both clinicians and dermatologists. Due to the importance of early diagnosis and in order to assist the dermatologists, we propose an automated framework based on ensemble learning methods and dermoscopy images to differentiate melanoma from dysplastic and benign lesions. The evaluation of our framework on the recent and public dermoscopy benchmark (PH2 dataset) indicates the potential of proposed method. Our evaluation, using only global features, revealed that ensembles such as random forest perform better than single learner. Using random forest ensemble and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.

An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images

This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works.

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

BackgroundSpectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers.MethodsThe dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024xa0pxxa0×xa0512xa0px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations.Results and conclusionBesides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: Application to DME detections

_{16 {text{-}} mathrm{ri}}

Classifying DME vs normal SD-OCT volumes: A review

16-ri vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.

On spatio-temporal saliency detection in videos using multilinear PCA

Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and overcome the inter-patients intensity variations. However, little attention has been dedicated to the normalization of T2W-Magnetic Resonance Imaging (MRI) prostate images. In this paper, we propose two methods to normalize T2W-MRI prostate images: (i) based on a Rician a priori and (ii) based on a Square-Root Slope Function (SRSF) representation which does not make any assumption regarding the Probability Density Function (PDF) of the data. A comparison with the state-of-the-art methods is also provided. The normalization of the data is assessed by comparing the alignment of the patient PDFs in both qualitative and quantitative manners. In both evaluation, the normalization using Rician a priori outperforms the other state-of-the-art methods.

Classification of SD-OCT Volumes with LBP: Application to DME Detection

This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.