Baidya Nath Saha

Quick detection of brain tumors and edemas: A bounding box method using symmetry

A significant medical informatics task is indexing patient databases according to size, location, and other characteristics of brain tumors and edemas, possibly based on magnetic resonance (MR) imagery. This requires segmenting tumors and edemas within images from different MR modalities. To date, automated brain tumor or edema segmentation from MR modalities remains a challenging, computationally intensive task. In this paper, we propose a novel automated, fast, and approximate segmentation technique. The input is a patient study consisting of a set of MR slices, and its output is a subset of the slices that include axis-parallel boxes that circumscribe the tumors. Our approach is based on an unsupervised change detection method that searches for the most dissimilar region (axis-parallel bounding boxes) between the left and the right halves of a brain in an axial view MR slice. This change detection process uses a novel score function based on Bhattacharya coefficient computed with gray level intensity histograms. We prove that this score function admits a very fast (linear in image height and width) search to locate the bounding box. The average dice coefficients for localizing brain tumors and edemas, over ten patient studies, are 0.57 and 0.52, respectively, which significantly exceeds the scores for two other competitive region-based bounding box techniques.

Image thresholding by variational minimax optimization

In this paper we introduce an adaptive image thresholding technique via minimax optimization of a novel energy functional that consists of a non-linear convex combination of an edge sensitive data fidelity term and a regularization term. While the proposed data fidelity term requires the threshold surface to intersect the image surface only at places with large image gradient magnitude, the regularization term enforces smoothness in the threshold surface. To the best of our knowledge, all the previously proposed energy functional-based adaptive image thresholding algorithms rely on manually set weighting parameters to achieve a balance between the data fidelity and the regularization terms. In contrast, we use minimax principle to automatically find this weighting parameter value, as well as the threshold surface. Our conscious choice of the energy functional permits a variational formulation within the minimax principle leading to a globally optimum solution. The proposed variational minimax optimization is carried out by an iterative gradient descent with exact line search technique that we experimentally demonstrate to be computationally far more attractive than the Fibonacci search applied to find the minimax solution. Our method shows promising results to preserve edge/texture structures in different benchmark images over other competing methods. We also demonstrate the efficacy of the proposed method for delineating lung boundaries from magnetic resonance imagery (MRI).

Automated white matter total lesion volume segmentation in diabetes.

BACKGROUND AND PURPOSE: WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes. MATERIALS AND METHODS: Structural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board–approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0–9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods. RESULTS: Correlation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of k = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at k = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden. CONCLUSIONS: We validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.

Snake Validation: A PCA-Based Outlier Detection Method

We utilize outlier detection by principal component analysis (PCA) as an effective step to automate snakes/active contours for object detection. The principle of our approach is straightforward: we allow snakes to evolve on a given image and classify them into desired object and non-object classes. To perform the classification, an annular image band around a snake is formed. The annular band is considered as a pattern image for PCA. Extensive experiments have been carried out on oil-sand and leukocyte images and the performance of the proposed method has been compared with two other automatic initialization and two gradient-based outlier detection techniques. Results show that the proposed algorithm improves the performance of automatic initialization techniques and validates snakes more accurately than other outlier detection methods, even when considerable object localization error is present.

Anovel framework for automatic passenger counting

Wepropose anovel framework for counting passengers in a railway station. The framework has three components: people detection, trackingand validation. We detect every person using Hough circle when he or she enters the field of view. The person is then tracked using optical flow until (s)he leaves the field of view. Finally, the tracker generated trajectory is validated through aspatio-temporal background subtraction technique. The number of valid trajectories provides passenger count. Each of the three components of the proposed framework has been compared with competitive methods on three datasets of varying crowd densities. Extensive experiments have been conducted on the datasets having top views of the passengers. Experimental results demonstrate that the proposed algorithmic framework performs well both on dense and sparse crowds and it can successfully detect and track persons with different hair colors, hoodies, caps, long winter jackets, bags and so on. The proposed algorithm shows promising results also for people moving in different directions. The proposed framework can detect up to 30% more accurately and 20% more precisely than other competitive methods.

Computing oil sand particle size distribution by snake-PCA algorithm

An important measure in various stages of oil sand mining is particle size distribution (PSD) of oil sand particles. Currently PSD is found by time consuming manual inspection. An effective automation of PSD computation can play a significant role in improving the mining process. Toward this goal we propose an algorithm (snake-PCA) to detect oil sands from conveyor belt images, which pose considerable challenges to automated analysis. The novelty in snake-PCA is as follows. First, snake-PCA evolves a number of snakes based on a novel variation of gradient vector flow requiring only a point as initialization. Oil sand is then detected by applying a threshold on PCA reconstruction error of a novel pattern image formed on each evolved snake. We show the discriminative property of the proposed pattern image here. Also, our detection experiments with snake-PCA produce a PSD matching well with a manually found PSD.

Change Detection and Object Segmentation: A Histogram of Features-Based Energy Minimization Approach

We consider here a change detection problem: to find regions of change on a test image with respect to a reference image. Unlike the state-of-the-art change detection and background subtraction algorithms that compute only local (pixel location-based) changes, we propose to minimize a novel region-based energy functional based on Bhattacharya coefficient involving histograms of image features. The optimization of the proposed energy functional simply consists of two very efficient searches if a crude segmentation such as a bounding box around the region of change is sufficient. Also, it allows variational optimization via level set-based curve evolution for supervised binary image labeling. The framework is demonstrated to cope well with considerable camera motion and shifts of objects between the test and the reference images. We illustrate encouraging results on finding bounding box around abnormality from brain MRI, object detection for maritime surveillance, and segmenting oil-sand particles from conveyor belt images.

AR-boost: reducing overfitting by a robust data-driven regularization strategy

We introduce a novel, robust data-driven regularization strategy called Adaptive Regularized Boosting (AR-Boost), motivated by a desire to reduce overfitting. We replace AdaBoosts hard margin with a regularized soft margin that trades-off between a larger margin, at the expense of misclassification errors. Minimizing this regularized exponential loss results in a boosting algorithm that relaxes the weak learning assumption further: it can use classifiers with error greater than 1/2. This enables a natural extension to multiclass boosting, and further reduces overfitting in both the binary and multiclass cases. We derive bounds for training and generalization errors, and relate them to AdaBoost. Finally, we show empirical results on benchmark data that establish the robustness of our approach and improved performance overall.

Automating snakes for multiple objects detection

Active contour or snake has emerged as an indispensable interactive image segmentation tool in many applications. However, snake fails to serve many significant image segmentation applications that require complete automation. Here, we present a novel technique to automate snake/active contour for multiple object detection. We first apply a probabilistic quad tree based approximate segmentation technique to find the regions of interest (ROI) in an image, evolve modifed GVF snakes within ROIs and finally classify the snakes into object and nonobject classes using boosting. We propose a novel loss function for boosting that is more robust to outliers concerning snake classification and we derive a modified Adaboost algorithm by minimizing the proposed loss function to achieve better classification results. Extensive experiments have been carried out on two datasets: one has importance in oil sand mining industry and the other one is significant in bio-medical engineering. Performances of proposed snake validation have been compared with competitive methods. Results show that proposed algorithm is computationally less expensive and can delineate objects up to 30% more accurately as well as precisely.

A robust convergence index filter for breast cancer cell segmentation

COnvergence INdex (COIN) filter, a successful tool for cell localization, evaluates the degree of convergence of the gradient vectors within the neighborhood (region of support) toward a pixel of interest. All previous efforts were to increase the adaptability of the region of support to make the COIN filter robust and accurate. However, improving the quality of the image gradient map was ignored, which results in poor performance of the members of the COIN family in noisy settings. We propose a new Robust Convergence Index (RCI) filter that tailors the COIN filter in a noisy environment by (a) spreading the gradient vectors within non-homogeneous object regions by convolving an Aggregated Edge Probability Map (AEPM) with an edge preserving gradient vector kernel, and (b) increasing the convergence of the gradient vectors through the integration of the sine and cosine distribution as well as the magnitude of the gradient vectors. AEPM is computed through the consensus of the responses of a number of edge detectors over a wide range of scales, which lessens the effects of clutter by enforcing higher weights to the actual edges, and a non-parametric Kernel Density Estimation (KDE) is used to compute the edge probability map. Experimental results demonstrate that it obtains state-of-the-art performance.