Shouvik Chakraborty

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

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 McCullochs method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsus method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsus between class variance, Kapurs 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 Kapurs entropy segmentation method based on the modified CS required the least computational time compared to Otsus 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.

Biomedical image enhancement based on modified Cuckoo Search and morphology

This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation. In recent years, various digital image processing techniques are developed. Computer Vision, machine interfaces, manufacturing industry, data compression for storage, vehicle tracking and many more are some of the domains of digital image processing application. In most of the cases, digital biomedical images contains various types of noise, artifacts etc. and are not useful for direct applications. Before using it in any process, the input image has to be gone through some preprocessing stages; such preprocessing is generally called as image enhancement. In this work, a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation. Presence of noise and other unwanted objects generates distortion in an image and it will affect the ultimate result of the process. In case of biomedical images, accuracy of the results is very important. It may also decrease the discernibility of many features inside the images. It can affect the classification accuracy. In this work, this issue has been targeted and improved by obtaining better contrast value after converting the color image into grayscale image. The basic property of the cuckoo search algorithm is that the amplitudes of its components are capable to objectively describe the contribution of the gray levels to the formation of image information for the best contrast value of a digital image. The proposed method modified the conventional cuckoo search method by employing the McCullochs method for levy flight generation. After computing the best contrast value, morphological operation has been applied. In morphological operation based phase, the intensity parameters are tuned for quality enhancement. Experimental results illustrate the effectiveness of this work.

Detection of skin disease using metaheuristic supported artificial neural networks

Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.

Application of Multiobjective Optimization Techniques in Biomedical Image Segmentation—A Study

Multiobjective optimization methods in image analysis are one of the active research domains in the current years. These methods are used for the decision-making process in case of image segmentation. Multiobjective techniques are popular and suitable model for many difficult optimization problems. In various practical problems, different objectives are to be considered. Now, most of the problems have some objectives those are conflicting in nature. Hence, only one objective cannot be optimized or prioritize because it can result in some adverse effect on other objective, and can produce some undesired results in terms of the other objectives. The main goal of this chapter is to give a comprehensive study of multiobjective optimization techniques in biomedical image analysis problem. The different models are categorized depending on the relevant features. For example, the different aspects of the optimization methods employed, different formulations of the problems, categories of data, and the domain of the application. This study mainly focuses on the multiobjective optimization techniques that can be used to analyze digital images specially biomedical images. Here, some of the problems, and challenges related to images are diagnosed and analyzed with multiple objectives. It is a comprehensive study that consolidated some of the recent works along with future directions.

A New and Resilient Image Encryption Technique Based on Pixel Manipulation, Value Transformation and Visual Transformation Utilizing Single–Level Haar Wavelet Transform

Lossless image cryptography is always preferred over lossy image cryptography. In this approach the authors have proposed a very resilient and novel image encryption/decryption algorithm. Initially the image is first converted to frequency components and the encryption is performed on sub-bands and the encrypting algorithm is found to be very strong, reliable and strong. The encryption algorithm involves pixel breakup into two parts and reversing parts of the pixel. The results show a deviation of pixel between the images present in the original and encrypted domains. The decryption algorithm is exactly the encryption algorithm in reverse. The proposed algorithm is evaluated by standard measures and it is seen to be attack-resistant to well-known attacks.