On Spatio-Temporal Saliency Detection in Videos using Multilinear PCA

International audienceVisual saliency is an attention mechanism which helps to focus on regions of interest instead of processing the whole image or video data. Detecting salient objects in still images has been widely addressed in literature with several formulations and methods. However, visual saliency detection in videos has attracted little attention, although motion information is an important aspect of visual perception. A common approach for obtaining a spatio-temporal saliency map is to combine a static saliency map and a dynamic saliency map. In this paper, we extend a recent saliency detection approach based on principal component analysis (PCA) which have shwon good results when applied to static images. In particular, we explore different strategies to include temporal information into the PCA-based approach. The proposed models have been evaluated on a publicly available dataset which contain several videos of dynamic scenes with complex background, and the results show that processing the spatio-tempral data with multilinear PCA achieves competitive results against state-of-the-art methods

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

This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Our method is based on Local Binary Patterns (LBP) features to describe the texture of Optical Coherence Tomography (OCT) images and we compare different LBP features extraction approaches to compute a single signature for the whole OCT volume. Experimental results with two datasets of respectively 32 and 30 OCT volumes show that regardless of using low or high level representations, features derived from LBP texture have highly discriminative power. Moreover, the experiments show that the proposed method achieves better classification performances than other recent published works

Tackling the Problem of Data Imbalancing for Melanoma Classification

International audienceMalignant melanoma is the most dangerous type of skin cancer, yet melanoma is the most treatable kind of cancer when diagnosed at an early stage. In this regard, Computer-Aided Diagnosis systems based on machine learning have been developed to discern melanoma lesions from benign and dysplastic nevi in dermoscopic images. Similar to a large range of real world applications encountered in machine learning, melanoma classification faces the challenge of imbalanced data, where the percentage of melanoma cases in comparison with benign and dysplastic cases is far less. This article analyzes the impact of data balancing strategies at the training step. Subsequently, Over-Sampling (OS) and Under-Sampling (US) are extensively compared in both feature and data space, revealing that NearMiss-2 (NM2) outperform other methods achieving Sensitivity (SE) and Specificity (SP) of 91.2% and 81.7%, respectively. More generally, the reported results highlight that methods based on US or combination of OS and US in feature space outperform the others

Classifying DME vs Normal SD-OCT volumes: A review

International audienceThis article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this common benchmark and dataset to produce reliable comparison

Classification of Melanoma Lesions Using Sparse Coded Features and Random Forests

International audienceMalignant melanoma is the most dangerous type of skin cancer, yet it is the most treatable kind of cancer, conditioned by its early diagnosis which is a challenging task for clinicians and dermatologists. In this regard, CAD systems based on machine learning and image processing techniques are developed to differentiate melanoma lesions from benign and dysplastic nevi using dermoscopic images. Generally, these frameworks are composed of sequential processes: pre-processing, segmentation, and classification. This architecture faces mainly two challenges: (i) each process is complex with the need to tune a set of parameters, and is specific to a given dataset; (ii) the performance of each process depends on the previous one, and the errors are accumulated throughout the framework. In this paper, we propose a framework for melanoma classification based on sparse coding which does not rely on any pre-processing or lesion segmentation. Our framework uses Random Forests classifier and sparse representation of three features: SIFT, Hue and Opponent angle histograms, and RGB intensities. The experiments are carried out on the public PH 2 dataset using a 10-fold cross-validation. The results show that SIFT sparse-coded feature achieves the highest performance with sensitivity and specificity of 100% and 90.3% respectively, with a dictionary size of 800 atoms and a sparsity level of 2. Furthermore, the descriptor based on RGB intensities achieves similar results with sensitivity and specificity of 100% and 71.3%, respectively for a smaller dictionary size of 100 atoms. In conclusion, dictionary learning techniques encode strong structures of dermoscopic images and provide discriminant descriptors

Normalization of T2W-MRI Prostate Images using Rician a priori

International audienceProstate 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 using Local Binary Patterns: Experimental Validation for DME Detection

International audienceThis paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (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 pre-processings in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and non-linear 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 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 pre-processing is inconsistent with respect to different classifiers and feature configurations

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

International audienceThis paper deals with the automated detection of DME on OCT volumes.Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan.Features such as HoG and LBP are extracted and combined to create a set of different feature vectors which are fed to a linear-SVM classifier.Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset

An approach to melanoma classification exploiting polarization information

Malignant melanoma is the deadliest of skin cancers and causes the majority of deaths in comparison to other skin-related malignancies. Yet it is the most treatable type of cancer, thanks to its early diagnosis. Subsequently, early diagnosis is crucial for patient survival rate and numerous Computer-Aided Diagnosis (CAD) systems have been proposed by the research community to assist dermatologists in early diagnosis. These systems are based on the most common skin imaging modality, cross-polarized dermoscopy. Cross-polarized dermoscopes (PD) allow for the visualization of the subsurface anatomic structure of the epidermis and papillary dermis and eliminate the specular reflection of the surface. Although this modality has been used extensively, the full potential of polarized measurements has not been realized in the field of skin imaging. This research first extensively analyzes different aspects of the automated classification of pigmented skin lesions (PSLs) and proposes a CAD system for automatic recognition of melanoma lesions based on the PD images. The proposed CAD system is evaluated over extensive experiments on two dermoscopic datasets. Later for further investigation of polarized imaging, a novel partial Stokes polarimeter system is proposed. This system is able to acquire polarized images of in-vivo PSLs and capture the epidermis and superficial dermal layers, where skin lesions are often originated. The polarized and dermoscopy properties of the acquired images are then analyzed to propose a new CAD system based on image polarimetry. The initial tests with the first prototype of Stokes polarimeter revealed the potential and benefits of such systems for providing additional information beyond RGB images acquired with PD devices. In order to acquire a wider clinical dataset and identify the drawbacks of the first prototype, this device is currently being used in the Melanoma Unit at the Clinic Hospital of Barcelona.El melanoma maligne és el més mortal dels càncers de pell i provoca la majoria de les morts en comparació amb altres tumors malignes relacionats amb la pell. No obstant això, és el tipus més tractable de càncer, gràcies al seu diagnòstic precoç. Per tant, el diagnòstic precoç és crucial per a la supervivència dels pacients. Nombrosos sistemes de diagnòstic assistit per ordinador (CAD, de l’anglés Computer Aided Diagnosis) han estat proposats per la comunitat investigadora per ajudar als dermatòlegs en el diagnósstic precoç. Aquests sistemes es basen en la modalitat més emprada d’adquisició d’imatges de pell, la dermatoscòpia de polarització creuada (PD, de l’angl`es Polarized Dermatoscopy). La dermatoscòpia de polarització creuada permet la visualització de l’estructura anatòmica del subsòl de l’epidermis i la dermis papil·lar, eliminant les reflexions especulars de la superfíıcie. Tot i que aquesta modalitat ha estat utilitzada àmpliament, no tot el potencial de les mesures polaritzades ha estat aprofitat en el camp de la imatge de pell. Aquest treball de recerca analitza, en primer lloc, diversos aspectes de la classificació automatitzada de les lesions cutànies pigmentades (PSLs, de l’anglès Pigmented Skin Lesions) i proposa un sistema CAD per al reconeixement automàtic de lesions de melanoma en base a les imatges de PD. El sistema CAD proposat es va avaluar en el transcurs d’extensos experiments en dos conjunts de dades dermatoscòpiques. Posteriorment, en una investigació més extensa pel que fa a la formació d’imatges polaritzades, es proposa un nou sistema de partial Stokes polarimeter. Aquest sistema és capaç d’adquirir imatges polaritzades dels PSLs en viu, capturant l’epidermis i les capes d´ermiques superficials, on sovint s’originen les lesions de la pell. Les propietats de polarització i dermoscopia de la imatge són analitzades a continuació, proposant un nou sistema CAD basat en la imatge de polarimetria. Les proves inicials, amb el primer prototip d’Stokes polarimeter, han revelat el potencial i els beneficis de tals sistemes per proporcionar informació addicional més enllà de les imatges RGB adquirides amb dispositius PD. Per tal d’adquirir un conjunt de dades clíniques més ampli i identificar els inconvenients del primer prototip, aquest dispositiu s’està utilitzant actualment a la Unitat de Melanoma de l’Hospital Clínic de Barcelona