Suvojit Acharjee

Bio: Suvojit Acharjee is an academic researcher from Jadavpur University. The author has contributed to research in topics: Motion vector & Block-matching algorithm. The author has an hindex of 14, co-authored 29 publications receiving 460 citations. Previous affiliations of Suvojit Acharjee include National Institute of Technology Agartala.

Video Shot Boundary Detection: A Review

Abstract: Video image processing is a technique to handle the video data in an effective and efficient way. It is one of the most popular aspects in the video and image based technologies such as surveillance. Shot change boundary detection is also one of the major research areas in video signal processing. Previous works have developed various algorithms in this domain. In this paper, a brief literature survey is presented that establishes an overview of the works that has been done previously. In this paper we have discussed few algorithms that were proposed previously which also includes histogram based, DCT based and motion vector based algorithms as well as their advantages and their limitations.

Multilevel Threshold Based Gray Scale Image Segmentation using Cuckoo Search

Abstract: Image Segmentation is a technique of partitioning the original image into some distinct classes. Many possible solutions may be available for segmenting an image into a certain number of classes, each one having different quality of segmentation. In our proposed method, multilevel thresholding technique has been used for image segmentation. A new approach of Cuckoo Search (CS) is used for selection of optimal threshold value. In other words, the algorithm is used to achieve the best solution from the initial random threshold values or solutions and to evaluate the quality of a solution correlation function is used. Finally, MSE and PSNR are measured to understand the segmentation quality. Keywords : Multilevel Image Segmentation, Correlation, Cuckoo Search, PSNR. 1. Introduction Image segmentation has a major importance in digital image processing. Image segmentation is actually the process of subdividing an image into its constituent regions or objects based on shape, colour, position, texture and homogeneity of image regions. In mathematical sense, the segmentation of image I, which is a set of pixels, is the partition of I into n disjoint sets M

Shape‐Based Approach for Coronary Calcium Lesion Volume Measurement on Intravascular Ultrasound Imaging and Its Association With Carotid Intima‐Media Thickness

Abstract: Objectives—Coronary calcification plays an important role in diagnostic classification of lesion subsets. According to histopathologic studies, vulnerable atherosclerotic plaque contains calcified deposits, and there can be considerable variation in the extent and degree of calcification. Intravascular ultrasound (IVUS) has demonstrated its role in imaging coronary arteries, thereby displaying calcium lesions. The aim of this work was to develop a fully automated system for detection, area and volume measurement, and characterization of the largest calcium deposits in coronary arteries. Furthermore, we demonstrate the correlation between the coronary calcium IVUS volume and the neu- rologic risk biomarker B-mode carotid intima-media thickness (IMT). Methods—Our system automatically detects the frames with calcium, identifies the largest calcium region, and performs shape-based volume measurements. The carotid IMT is measured by using AtheroEdge software (AtheroPoint, LLC) on B-mode ultra- sound imaging. Results—Our database consists of low-contrast IVUS videos and corresponding B-mode images from 100 patients. Our experiments showed that the correlation between calcium volumes and carotid IMT was higher for the left carotid artery compared to the right carotid artery (r = 0.066 for the left carotid artery and 0.121 for the right carotid artery). We obtained 97% accuracy for automated calcium detection compared against the scoring given by our expert radiologists. Furthermore, we benchmarked shape-based volume measurement against the conventional method, which used inte- gration of regions and showed a correlation of 84%. Conclusions—Since carotid IMT is an independent prognostic factor for myocardial infarction, and calcium lesions are correlated with stroke risk, we believe that this auto- mated system for calcium volume measurement could be useful for assessing patients' cardiovascular risk

Ant Weight Lifting algorithm for image segmentation

Abstract: Image segmentation forms a quintessential concept and is one of the most in-demand arenas of research in the field of image processing. Throughout the years several techniques like k-means clustering, watershed segmentation and quad tree segmentation have been devised to properly segment an image into well-defined classes. Segmentation techniques can be broadly classified as thresholding techniques, edge detection techniques, clustering, region based and matching. Image segmentation may be the ultimate output desired and may also be a penultimate step in the algorithm. In either case it becomes essential to get an accurately segmented image which is often not the case with the existing algorithms since each algorithm has its own drawback. In our paper we have proposed a novel segmentation technique that is bio-inspired from the behavioral nature of ants and is hence called the Ant Weight Lifting (AWL) segmentation algorithm. Our segmentation algorithm has generated optimum results on a wide range of test cases imposed by us with a high correlation factor between the original and segmented image and also an added perk in the form of a low time complexity.

Automated segmental-IMT measurement in thin/thick plaque with bulb presence in carotid ultrasound from multiple scanners

Abstract: Automated detection of the carotid bulb edge, which is considered a reference marker for measurements of the cIMT.Automated segment-based cIMT measurement system which estimates the cIMT for different segments of the carotid artery proximal to the bulb edge.Segmental-IMT (sIMT) allows us to measure IMT in 10 mm segments (namely: s1, s2 and s3) proximal to the bulb edge.The proposed fully automated bulb detection system achieved 92.67% precision against ideal bulb edge locations in the bulb transition zone and holds a significant promise for risk stratification tool for carotid disease. Background and objectivesStandardization of the carotid IMT requires a reference marker in ultrasound scans. It has been shown previously that manual reference marker and manually created carotid segments are used for measuring IMT in these segments. Manual methods are tedious, time consuming, subjective, and prone to errors. Bulb edge can be considered as a reference marker for measurements of the cIMT. However, bulb edge can be difficult to locate in ultrasound scans due to: (a) low signal to noise ratio in the bulb region as compared to common carotid artery region; (b) uncertainty of bulb location in craniocaudal direction; and (c) variability in carotid bulb shape and size. This paper presents an automated system (a class of AtheroEdge system from AtheroPoint, Roseville, CA, USA) for locating the bulb edge as a reference marker and further develop segmental-IMT (sIMT) which measures IMT in 10mm segments (namely: s1, s2 and s3) proximal to the bulb edge. MethodsThe patented methodology uses an integrated approach which combines carotid geometry and pixel-classification paradigms. The system first finds the bulb edge and then measures the sIMT proximal to the bulb edge. The system also estimates IMT in bulb region (bIMT). The 649 image database consists of varying plaque (light, moderate to heavy), image resolutions, shapes, sizes and ethnicity. ResultsOur results show that the IMT contributions in different carotid segments are as follows: bulb-IMT 34%, s1-IMT 29.46%, s2-IMT 11.48%, and s3-IMT 12.75%, respectively. We compare our automated results against reader's tracings demonstrating the following performance: mean lumen-intima error: 0.01235 0.01224mm, mean media-adventitia error: 0.020933 0.01539mm and mean IMT error: 0.01063 0.0031mm. Our system's Precision of Merit is: 98.23%, coefficient of correlation between automated and Reader's IMT is: 0.998 (p-value < 0.0001). These numbers are improved compared to previous publications by Suri's group which is automated multi-resolution conventional cIMT. ConclusionsOur fully automated bulb detection system reports 92.67% precision against ideal bulb edge locations as marked by the reader in the bulb transition zone.

Multi-level image thresholding using Otsu and chaotic bat algorithm

Abstract: Multi-level thresholding is a helpful tool for several image segmentation applications Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu's thresholding In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu's between-class variance and a novel chaotic bat algorithm (CBA) Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321) Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives Therefore, it can be applied in complex image processing such as automatic target recognition

Object-Level Video Advertising: An Optimization Framework

Abstract: In this paper, we present new models and algorithms for object-level video advertising. A framework that aims to embed content-relevant ads within a video stream is investigated in this context. First, a comprehensive optimization model is designed to minimize intrusiveness to viewers when ads are inserted in a video. For human clothing advertising, we design a deep convolutional neural network using face features to recognize human genders in a video stream. Human parts alignment is then implemented to extract human part features that are used for clothing retrieval. Second, we develop a heuristic algorithm to solve the proposed optimization problem. For comparison, we also employ the genetic algorithm to find solutions approaching the global optimum. Our novel framework is examined in various types of videos. Experimental results demonstrate the effectiveness of the proposed method for object-level video advertising.

Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search

Abstract: Authentication is very important in validating a medical content in the domain of telemedicine; however, there are many challenges. Accurate verification is paramount, and any misuse of personal information may have serious consequences. Many authentication processes tried to design various methods to minimise such discrepancies. In this current work, we propose a new approach to design a robust biomedical content authentication system by embedding logo of the hospital within the electrocardiogram signal by means of both discrete wavelet transformation and cuckoo search CS. An adaptive meta-heuristic cuckoo search is used to find the optimal scaling factor settings for logo embedding. Results show that the proposed method can serve as a secure and accurate authentication system.

Social Group Optimization Supported Segmentation and Evaluation of Skin Melanoma Images

Abstract: The segmentation of medical images by computational methods has been claimed by the medical community, which has promoted the development of several algorithms regarding different tissues, organs and imaging modalities. Nowadays, skin melanoma is one of the most common serious malignancies in the human community. Consequently, automated and robust approaches have become an emerging need for accurate and fast clinical detection and diagnosis of skin cancer. Digital dermatoscopy is a clinically accepted device to register and to investigate suspicious regions in the skin. During the skin melanoma examination, mining the suspicious regions from dermoscopy images is generally demanded in order to make a clear diagnosis about skin diseases, mainly based on features of the region under analysis like border symmetry and regularity. Predominantly, the successful estimation of the skin cancer depends on the used computational techniques of image segmentation and analysis. In the current work, a social group optimization (SGO) supported automated tool was developed to examine skin melanoma in dermoscopy images. The proposed tool has two main steps, mainly the image pre-processing step using the Otsu/Kapur based thresholding technique and the image post-processing step using the level set/active contour based segmentation technique. The experimental work was conducted using three well-known dermoscopy image datasets. Similarity metrics were used to evaluate the clinical significance of the proposed tool such as Jaccard’s coefficient, Dice’s coefficient, false positive/negative rate, accuracy, sensitivity and specificity. The experimental findings suggest that the proposed tool achieved superior performance relatively to the ground truth images provided by a skin cancer physician. Generally, the proposed SGO based Kapur’s thresholding technique combined with the level set based segmentation technique is very effective for identifying melanoma dermoscopy digital images with high sensitivity, specificity and accuracy.

Big Data Knowledge System in Healthcare

Abstract: The health care systems are rapidly adopting large amounts of data, driven by record keeping, compliance and regulatory requirements, and patient care. The advances in healthcare system will rapidly enlarge the size of the health records that are accessible electronically. Concurrently, fast progress has been made in clinical analytics. For example, new techniques for analyzing large size of data and gleaning new business insights from that analysis is part of what is known as big data. Big data also hold the promise of supporting a wide range of medical and healthcare functions, including among others disease surveillance, clinical decision support and population health management. Hence, effective big data based knowledge management system is needed for monitoring of patients and identify the clinical decisions to the doctor. The chapter proposes a big data based knowledge management system to develop the clinical decisions. The proposed knowledge system is developed based on variety of databases such as Electronic Health Record (EHR), Medical Imaging Data, Unstructured Clinical Notes and Genetic Data. The proposed methodology asynchronously communicates with different data sources and produces many alternative decisions to the doctor.