Diverse lesion detection from retinal images by subspace learning over normal samples

Abstract Lesion detection from retinal images is an important topic in the retinal image analysis. Many computer-aided detection techniques have been developed for detecting retinal lesions. However, these techniques are mainly used to detect specific lesion types from retinal images. They cannot be applied to detect diverse types of lesions from retinal images, which is a challenging task because lesion number and types in retinal images are generally unknown in advance, and different lesions may exhibit diverse properties in shapes, sizes, colors, textures and positions. Inspired by the doctors’ visual diagnostic mode, this paper proposes a novel computational framework to detect various types of lesions from retinal images. In this framework, many healthy fundus images are collected to act as ”doctors’ detection experience”, and local visual properties of lesions are used to distinguish true positives from false positives. A specific subspace is learned from the collected normal set and acts as a specific structural filter, by which various lesions in a retinal image can be filtered out while other normal regions keep little changes. By computing the difference image between a target image and its filtered image, different types of lesion candidates can be separated from the image. Furthermore, based on local visual context properties of lesions, the true lesions are identified from the lesion candidates. Extensive experiments have shown that the proposed method can more effectively detect diverse lesions from retinal images compared with related methods.

[1]  Farida Cheriet,et al.  Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening , 2016, IEEE Transactions on Medical Imaging.

[2]  Gongping Yang,et al.  Hierarchical retinal blood vessel segmentation based on feature and ensemble learning , 2015, Neurocomputing.

[3]  András Hajdu,et al.  Retinal Microaneurysm Detection Through Local Rotating Cross-Section Profile Analysis , 2013, IEEE Transactions on Medical Imaging.

[4]  Bram van Ginneken,et al.  Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images , 2016, IEEE Transactions on Medical Imaging.

[5]  Sharib Ali,et al.  Statistical atlas based exudate segmentation , 2013, Comput. Medical Imaging Graph..

[6]  György Kovács,et al.  A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction , 2016, Medical Image Anal..

[7]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[8]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[9]  Jayanthi Sivaswamy,et al.  A Successive Clutter-Rejection-Based Approach for Early Detection of Diabetic Retinopathy , 2011, IEEE Transactions on Biomedical Engineering.

[10]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Gwénolé Quellec,et al.  Automatic detection of referral patients due to retinal pathologies through data mining , 2016, Medical Image Anal..

[12]  Kenneth W. Tobin,et al.  Exudate-based diabetic macular edema detection in fundus images using publicly available datasets , 2012, Medical Image Anal..

[13]  Samaneh Abbasi-Sureshjani,et al.  Boosted Exudate Segmentation in Retinal Images Using Residual Nets , 2017, FIFI/OMIA@MICCAI.

[14]  J. Wainer,et al.  Advancing Bag-of-Visual-Words Representations for Lesion Classification in Retinal Images , 2014, PloS one.

[15]  Neda Bernasconi,et al.  Segmentation of focal cortical dysplasia lesions on MRI using level set evolution , 2006, NeuroImage.

[16]  Roberto Hornero,et al.  Retinal image analysis based on mixture models to detect hard exudates , 2009, Medical Image Anal..

[17]  Gwénolé Quellec,et al.  Automated early detection of diabetic retinopathy. , 2010, Ophthalmology.

[18]  Sarah H. Creem-Regehr,et al.  Visual Perception from a Computer Graphics Perspective , 2011 .

[19]  Joseph M. Reinhardt,et al.  Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images , 2013, IEEE Transactions on Medical Imaging.

[20]  George Azzopardi,et al.  Trainable COSFIRE filters for vessel delineation with application to retinal images , 2015, Medical Image Anal..

[21]  Koenraad Van Leemput,et al.  Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.

[22]  Daniel Rueckert,et al.  Multiple Sclerosis Lesion Segmentation Using Dictionary Learning and Sparse Coding , 2013, MICCAI.

[23]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[24]  M. Sonka,et al.  Retinal Imaging and Image Analysis. , 2010, IEEE transactions on medical imaging.

[25]  S. Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, CVPR 2009.

[26]  Gwénolé Quellec,et al.  Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs , 2008, IEEE Transactions on Medical Imaging.

[27]  Gwénolé Quellec,et al.  A multiple-instance learning framework for diabetic retinopathy screening , 2012, Medical Image Anal..

[28]  J. Dheeba,et al.  Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System , 2015, Journal of Digital Imaging.

[29]  Xiaoming Liu,et al.  Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs , 2017, Artif. Intell. Medicine.

[30]  Desire Sidibé,et al.  Discrimination of retinal images containing bright lesions using sparse coded features and SVM , 2015, Comput. Biol. Medicine.

[31]  Andrew Zisserman,et al.  Deep Features for Text Spotting , 2014, ECCV.

[32]  U. Rajendra Acharya,et al.  Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach , 2013, Knowl. Based Syst..

[33]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[34]  Shehzad Khalid,et al.  Identification and classification of microaneurysms for early detection of diabetic retinopathy , 2013, Pattern Recognit..

[35]  Sven Loncaric,et al.  Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion , 2016, Comput. Methods Programs Biomed..

[36]  H. Abdi,et al.  Principal component analysis , 2010 .

[37]  Jayanthi Sivaswamy,et al.  Visual saliency based bright lesion detection and discrimination in retinal images , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[38]  Hou Zhiqiang.  Threshold Selection Tactics for an Edge Detection Based on Vision Model , 2004 .

[39]  Jayanthi Sivaswamy,et al.  Detection and discrimination of disease-related abnormalities based on learning normal cases , 2012, Pattern Recognit..