Gorthi R. K. Sai Subrahmanyam

A Recursive Filter for Despeckling SAR Images

This correspondence proposes a recursive algorithm for noise reduction in synthetic aperture radar imagery. Excellent despeckling in conjunction with feature preservation is achieved by incorporating a discontinuity-adaptive Markov random field prior within the unscented Kalman filter framework through importance sampling. The performance of this method is demonstrated on both synthetic and real examples.

Recursive framework for joint inpainting and de-noising of photographic films

We address the problem of inpainting noisy photographs. We present a recursive image recovery scheme based on the unscented Kalman filter (UKF) to simultaneously inpaint identified damaged portions in an image and suppress film-grain noise. Inpainting of the missing observations is guided by a mask-dependent reconstruction of the image edges. Prediction within the UKF is based on a discontinuity-adaptive Markov random field prior that attempts to preserve edges while achieving noise reduction in uniform regions. We demonstrate the capability of the proposed method with many examples.

Framework for morphometric classification of cells in imaging flow cytometry

Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high‐throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi‐automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph‐based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label‐free leukaemia cell‐lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost‐effective microfluidics‐based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost‐effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis.

Importance Sampling Kalman Filter for Image Estimation

This paper presents discontinuity adaptive image estimation within the Kalman filter framework by non-Gaussian modeling of the image prior. A generalized methodology is proposed for specifying state-dynamics using the conditional density of the state given its neighbors, without explicitly defining the state equation. The novelty of our approach lies in directly obtaining the predicted mean and variance of the non-Gaussian state conditional density by importance sampling and incorporating them in the update step of the Kalman filter. Experimental results are given to demonstrate the effectiveness of the proposed method in preserving edges.

Convolutional Neural Network-based malaria Diagnosis from Focus Stack of Blood Smear Images Acquired Using Custom-built Slide Scanner

The present paper introduces a focus stacking-based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2-level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand-engineered features. The slide images are acquired with a custom-built portable slide scanner made from low-cost, off-the-shelf components and is suitable for point-of-care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis.

Phase unwrapping with Kalman filter based denoising in digital holographic interferometry

Phase information recovered through interferometric techniques is mathematically wrapped in the interval (-π, π). Obtaining the original unwrapped phase is very important in numerous number of applications. This paper discusses a Fourier transform based phase unwrapping method. Kalman filter is proposed for denoising in post processing step to restore the unwrapped phase without any noise. The proposed method is highly robust to noise and performs better even at lower SNR values (5-10dB) with a very less value of RMS error. Also, the time taken for execution is very less compared to the many available methods in the literature.

Edge-preserving unscented Kalman filter for speckle reduction

We propose a recursive spatial-domain speckle reduction algorithm for synthetic aperture radar (SAR) imagery based on the unscented Kalman filter (UKF) with a discontinuity-adaptive Markov random field (DAMRF) prior. The capability of the UKF in handling speckle noise and the feature preservation ability of the DAMRF model are explored within a unified framework through importance sampling.

Unscented Kalman Filter for Image Estimation in Film-Grain Noise

This paper presents a novel approach based on the unscented Kalman filter (UKF) for image estimation in film-grain noise. The image prior is modeled as non-Gaussian and is incorporated within the UKF frame work using importance sampling. A small carefully chosen deterministic set of sigma points is used to capture the prior and is propagated through film-grain nonlinearity to compute image statistics. Experimental results are given to demonstrate the efficacy of the proposed method.

Cytopathological image analysis using deep-learning networks in microfluidic microscopy

Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60). The cell images used in the classification are captured using a low-cost, high-throughput cell imaging technique: microfluidics-based imaging flow cytometry. We demonstrate that without any conventional fine segmentation followed by explicit feature extraction, the proposed deep-learning algorithms effectively classify the coarsely localized cell lines. We show that the designed deep belief network as well as the deeply pretrained convolutional neural network outperform the conventionally used decision systems and are important in the medical domain, where the availability of labeled data is limited for training. We hope that our work enables the development of a clinically significant high-throughput microfluidic microscopy-based tool for disease screening/triaging, especially in resource-limited settings.

Correlation-Based Tracker-Level Fusion for Robust Visual Tracking

Although visual object tracking algorithms are capable of handling various challenging scenarios individually, none of them are robust enough to handle all the challenges simultaneously. For any online tracking by detection method, the key issue lies in detecting the target over the whole frame and updating systematically a target model based on the last detected appearance to avoid the drift phenomenon. This paper aims at proposing a novel robust tracking algorithm by fusing the frame level detection strategy of tracking, learning, & detection with the systematic model update strategy of Kernelized Correlation Filter tracker. The risk of drift is mitigated by the fact that the model updates are primarily driven by the detections that occur in the spatial neighborhood of the latest detections. The motivation behind the selection of trackers is their complementary nature in handling tracking challenges. The proposed algorithm efficiently combines the two state-of-the-art tracking algorithms based on conservative correspondence measure with strategic model updates, which takes advantages of both and outperforms them on their short ends by virtue of other. Extensive evaluation of the proposed method based on different metrics is carried out on the data sets ALOV300++, Visual Tracker Benchmark, and Visual Object Tracking. We demonstrated its performance in terms of robustness and success rate by comparing with state-of-the-art trackers.