Kalyani Mali

Bio: Kalyani Mali is an academic researcher from Kalyani Government Engineering College. The author has contributed to research in topics: Image segmentation & Cluster analysis. The author has an hindex of 18, co-authored 101 publications receiving 923 citations.

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

Abstract: Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity It is also valuable in foremost meadows of technology and medicine Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely Different segments should be identified accurately in order to identify and to count cells in a microscope image Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells Thus, a novel method based on cuckoo search after pre-processing step is employed The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells The proposed method can be applied on the color images directly The proposed approach incorporates the McCulloch's method for levy flight production in cuckoo search (CS) algorithm Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR

Fuzzy-based artificial bee colony optimization for gray image segmentation

Abstract: In this article, we have proposed an image segmentation algorithm FABC, which is a kind of unsupervised classification (clustering), where we combine the concept of artificial bee colony optimization (ABC) and the popular fuzzy C means (FCM) and named it as fuzzy-based ABC or FABC. In FABC, we have used fuzzy membership function to search for optimum cluster centers using ABC. FABC is more efficient than other optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and expectation maximization (EM) algorithms. FABC overcomes the drawbacks of FCM as it does not depend on the choice of initial cluster centers and it performs better in terms of convergency, time complexity, robustness and segmentation accuracy. FABC becomes more efficient as it takes the advantage of the randomized characteristics of ABC for the initialization of the cluster centers. The experiments with FABC, GA, PSO and EM have been done over various grayscale images including some synthetic, medical and texture images, and segmentation of such images is very difficult due to the low contrast, noise and other imaging ambiguities. The efficiency of FABC is proven by both quantitative and qualitative measures.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Computer Vision, Graphics, and Image Processing

Abstract: Most of the past document image watermarking schemes focus on providing same level of integrity and copyright protection for information present in the source document image. However, in a document image the information contents possess various levels of sensitivity. Each level of sensitivity needs different type of protection and this demands multiple watermarking techniques. In this paper, a novel intelligent multiple watermarking techniques are proposed. The sensitivity of the information content of a block is based on the homogeneity and relative energy contribution parameters. Appropriate watermarking scheme is applied based on sensitivity classification of the block. Experiments are conducted exhaustively on documents. Experimental results reveal the accurate identification of the sensitivity of information content in the block. The results reveal that multiple watermarking schemes has reduced the amount of data to be embedded and consequently improved perceptual quality of the watermarked image.

Method for Determining the Optimal Number of Clusters Based on Agglomerative Hierarchical Clustering

Abstract: It is crucial to determine the optimal number of clusters for the clustering quality in cluster analysis. From the standpoint of sample geometry, two concepts, i.e., the sample clustering dispersion degree and the sample clustering synthesis degree, are defined, and a new clustering validity index is designed. Moreover, a method for determining the optimal number of clusters based on an agglomerative hierarchical clustering (AHC) algorithm is proposed. The new index and the method can evaluate the clustering results produced by the AHC and determine the optimal number of clusters for multiple types of datasets, such as linear, manifold, annular, and convex structures. Theoretical research and experimental results indicate the validity and good performance of the proposed index and the method.