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.

Abstract

In this work, we propose an online handwriting solution, where the data is captured with the help of depth sensors. Users may write in the air and our method recognizes it in real time using the proposed feature representation. Our method uses an efficient fingertip tracking approach and reduces the necessity of pen-up/pen-down switching. We validate our method on two depth sensors, Kinect and Leap Motion Controller. On a dataset collected from 20 users, we achieve a recognition accuracy of 97.59% for character recognition. We also demonstrate how this system can be extended for lexicon recognition with reliable performance. We have also prepared a dataset containing 1,560 characters and 400 words with the intention of providing common benchmark for handwritten character recognition using depth sensors and related research.

Abstract

Optic nerve head (ONH) segmentation problem has been 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 problem of segmentation involves segmentation of optic disk and cup region within ONH region. Available datasets do not incorporate challenges present in this problem. In this data paper, we present a comprehensive dataset of retinal images which include both normal and glaucomatous eyes and manual segmentations from multiple human experts. Both area and boundary-based evaluation measures are presented to evaluate a method on various aspects relevant to the problem of glaucoma assessment.

Abstract

Optic disk (OD) and macula are anatomical structures in retinal images. Their detection is of interest in computer assisted diagnostic (CAD) algorithm development. We propose a joint detection algorithm which exploits the bilateral symmetry present in retinal images and present a method to detect OD and macula. A vessel density based metric is proposed to determine the axis of symmetry which is used along with appearance features and geometric constraint on the OD and macula for their detection. The proposed method has been evaluated on a large number of images drawn from 4 public and one private dataset. Obtained results show that our method has consistently high detection rate across the datasets. Since the detection is also very fast, the proposed method offers a good solution for CAD based disease screening.

Abstract

We present a novel framework for depth based optic cup boundary extraction from asingle2D color fundus photograph per eye.Multiple depth estimates from shading, color and texture gradients in the image are correlated with Optical Coherence Tomography (OCT)based depth using a coupled sparse dictionary, trained on image-depth pairs. Finally, a Markov Random Field is formulated on the depth map to model the relative depth and discontinuity at the cup boundary. Leave-one-out validation of depth estimation on the INSPIRE dataset gave average correlation coefficient of 0.80. Our cup segmentation out performs several state-of-the-art methods on the DRISHTI-GS dataset with an average F-score of 0.81 and boundary-error of 21.21 pixels on test set against manual expert markings. Evaluation on an additional set of 28images against OCT scanner provided ground truth showed an average rms error of 0.11 on Cup-Disk diameter and 0.19 on Cup-disk area ratios.