Abstract

Color fundus photographs of 2252 eyes from1126 subjects were collected from 2 centers: Aravind Eye Hospital,Madurai and Coimbatore, India. The images of 1926 eyes (963 subjects) were used to train an automated image analysis-based system,which was developed to provide a decision on a given fundus image.A total of 163 subjects were clinically examined by 2 ophthalmologists independently and their diagnostic decisions were recorded. The consensus decision was defined to be the clinical reference (gold standard). Fundus images of eyes with disagreement in diagnosis were excluded from the study. The fundus images of the remaining 314eyes (157 subjects) were presented to 4 graders and their diagnostic decisions on the same were collected. The performance of the system was evaluated on the 314 images, using the reference standard. The sensitivity and specificity of the system and 4 independent graders were determined against the clinical reference standard

Abstract

Circular arc Radon (CAR) transforms associate to a function, its integrals along arcs of circles. The inversion of such transforms is of natural interest in several imaging modalities such as thermoacoustic and photoacoustic tomography, ultrasound, and intravascular imaging. Unlike the full circle counterpart—the circular Radon transform—which has attracted significant attention in recent years, the CAR transforms are scarcely studied objects.In this paper, we present an efficient algorithm for the numerical inversion of the CAR transform with fixed angular span, for the cases in which the support of the function lies entirely inside or outside the acquisition circle. The numerical algorithm is noniterative and is very efficient as the entire scheme, once processed,can be stored and used repeatedly for reconstruction of images. A modified numerical inversion algorithm is also presented to reduce the artifacts in the reconstructed image which are induced due to the limited angular span.

Abstract

3D imaging modalities are becoming increasingly popular and relevant in retinal imaging owing to their effectiveness in highlighting structures in sub-retinal layers. OCT is one such modality which has great importance in the context of analysis of cystoid structures in subretinal layers. Signal to noise ratio(SNR) of the images obtained from OCT is less and hence automated and accurate determination of cystoid structures from OCT is a challenging task. We propose an auto-mated method for detecting/segmenting cysts in 3D OCT volumes. The proposed method is biologically inspired and fast aided by the domain-knowledge about the cystoid structures. An ensemble learning method-Random forests is learnt for classification of detected region into cyst region. The method achieves detection and segmentation in a unified set-ting. We believe the proposed approach with further improvements can be a promising starting point for more robust approach. This methodis validated against the training set achieves a mean dice coefficient of0.3893 with a standard deviation of 0.2987

Abstract

Saliency computation is widely studied in computer vision but not in medical imaging. Existing computational saliency models have been developed for general (natural) images and hence may not be suitable for medical images. This is due to the variety of imaging modalities and the requirement of the models to capture not only normal but also deviations from normal anatomy. We present a biologically inspired model for colour fundus images and illustrate it for the case of diabetic retinopathy. The proposed model uses spatially-varying morphological operations to enhance lesions locally and combines an ensemble of results, of such operations, to generate the saliency map. The model is validated against an average Human Gaze map of 15 experts and found to have 10% higher recall (at 100% precision) than four leading saliency models proposed for natural images. The F-score for match with manual lesion markings by 5 experts was0.4 (as opposed to 0.532 for gaze map) for our model and very poor for existing models. The model’s utility is shown via a novel enhancement method which employs saliency to selectively enhance the abnormal regions and this was found to boost their contrast to noise ratio by∼30%

Abstract

Optic nerve head (ONH) segmentation problem is of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The assessment involves segmentation of optic disk and cup region within the ONH. In this paper, we present a comprehensive dataset of retinal images of both normal and glaucomatous eyes with manual segmentations from multiple human experts. The dataset also provides expert opinion on an image representing a normal or glaucomatous eye and on the presence of notching in an image. Several state of the art methods are assessed against this dataset using cup to disc diameter ratio (CDR), area and boundary-based evaluation measures. These are presented to aid benchmarking of new methods. A supervised, notch detection method based on the segmentation results is also proposed and its assessment results are included for benchmarking.

Abstract

Annotated data is critical for the development of many computer assisted diagnostic (CAD) algorithms. The process of manual annotation is very strenuous, time-consuming and an expensive component in CAD development. In this paper,we propose the idea of an interactive Assistive Annotation System (AAS) aimed at helping annotators by automatically marking possible regions of interest for further refinement by an annotator. We propose an unsupervised, biologically inspired method for bright lesion annotation. The performance of the proposed system has been evaluated against region-level ground truth in DiaretDB1 dataset and was found to have a sensitivity of60%at7false positives per image. Preliminary testing was also done on public datasets which do not provide any lesion level annotations. A visual assessment of the obtained results affirm a good agreement with lesions visible in images. The system with a simple modification is shown to have the potential to handle dark lesion annotation,which is a significantly more challenging problem. Thus, the proposed system is a good starting point for exploring the AAS idea for retinal images. Such systems can help extend the use of many existing datasets by enriching the image-level annotations with localised information.

Abstract

Nuclear imaging modalities like Positron emission tomography (PET) are characterized by a low SNR value due to the underlying signal generation mechanism. Given the significant role images play in current-day diagnostics, obtaining noise-free PET images is of great interest. With its higher packing density and larger and symmetrical neighbourhood,the hexagonal lattice offers a natural robustness to degradation in signal. Based on this observation, we propose an alternate solution to denoising, namely by changing the sampling lattice. We use filtered back projection for reconstruction, fol-lowed by a sparse dictionary based denoising and compare noise-free reconstruction on the Square and Hexagonal lattices. Experiments with PET phantoms (NEMA, Hoffman)and the Shepp-Logan phantom show that the improvement indenoising, post reconstruction, is not only at the qualitative but also quantitative level. The improvement in PSNR in the hexagonal lattice is on an average between 2 to 10 dB. These results establish the potential of the hexagonal lattice for re-construction from noisy data, in general.

Abstract

We propose a fast and accurate method for counting themitotic figures from histopathological slides using regenerative random forest. Our method performs automatic feature selection in an integratedmanner with classification. The proposed random forest assigns a weight to each feature (dimension) of the feature vector in a novel manner based on the importance of the feature (dimension). The forest also assigns amis classification-based penalty term to each tree in the forest. The trees are then regenerated to make a new population of trees (new forest)and only the more important features survive in the new forest. The feature vector is constructed from domain knowledge using the intensity features of nucleus, features of nuclear membrane and features of the possible stroma region surrounding the cell. The use of domain knowledge improves the classification performance. Experiments show at least 4%improvement in F-measure with an improvement in time complexity on the MITOS dataset from ICPR 2012 grand challenge

Abstract

Abnormality detection in medical images is a critical problem across image modalities and organs. Many ap-proaches to automatic abnormality detection use discrim-inative methods, based on domain knowledge, to address the problem. In this paper, we investigate the effective-ness of a generative model with no assumption of domain knowledge. We propose a method for classification of tis-sues based on Latent Factor analysis, and demonstrate iton colour retinal images with Diabetic Retinopathy-related abnormalities. A generative model based on Gaussian la-tent dictionaries is used to model various structures present at a patch level in an image. The model is used to classify a given patch into one of 5 classes: namely dark and bright lesions, neo-vascularisation (NV), plain tissue background and background with vessel. Evaluation of the proposed method was done on 3 different datasets. Same and crossdataset validation of the method yields an area under the receiver operating characteristic curve (AUC) of 0.85 for abnormality detection. The method was also modified to address a challenging problem of NV detection by posing it as a 2-class problem and the AUC for the same was found to be 0.92. This establishes the potential of LF model for abnormality detection.

Abstract

Accurate detection of optic disk and macula areof interest in automated analysis of retinal images as they are landmarks in retina and their detection aids in assessing these verity of diseases based on the locations of abnormalities relative to these landmarks. The general strategy is to design different methods to these landmarks. In contrast, we propose a novel and unified approach for Optic disk and macula detection in this paper using the Generalized Motion Pattern (GMP)[10] [19] which is derived by inducing motion to an image to smooth out unwanted information. The proposed method is unsupervised, parallelizable and handles illumination differences efficiently but assumes a fixed protocol in image acquisition. The proposed method has been tested on five public datasets and obtained results indicate comparable performance to supervised approaches for the same problem.