Krishna K

Bio: Krishna K is an academic researcher. The author has contributed to research in topics: Simulated annealing & Rate-monotonic scheduling. The author has an hindex of 1, co-authored 1 publications receiving 9 citations.

Biomedical image enhancement based on modified Cuckoo Search and morphology

Abstract: 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 McCulloch's 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.

Contrast Optimization using Elitist Metaheuristic Optimization and Gradient Approximation for Biomedical Image Enhancement

Abstract: Biomedical image analysis is one of the most challenging and inevitable part of the computer aided diagnostic systems. Automated analysis of the image can detect various diseases automatically without human intervention. Computer vision and artificial intelligence can sometimes defeat human diagnostic power and can reveal some hidden information from the biomedical images. In the field of health care, accurate results are highly required within stipulated amount of time. But to increase accuracy, proper preprocessing with sophisticated algorithms is required. Low quality image can affect processing algorithm which can leads to the poor result. Therefore, sophisticated preprocessing methods are required to get reliable results. Contrast is one of the most important parameter for any image. Poor contrast may cause several problems for computer vision algorithms. Conventional algorithms for contrast adjustment may not be suitable for many purposes. Sometimes, these methods can generate some images that may lose some critical information. In this work, a contrast optimization method based on well-known metaheuristic technique called genetic algorithm with elitism is used that can enhance the biomedical images for better analysis. A new kernel has been proposed to detect the edges. Obtained results illustrate the efficiency of the proposed algorithm.

Gradient approximation in retinal blood vessel segmentation

Abstract: Image segmentation is one of the major research domains in several applications including retinal blood vessel segmentation, which is an active research area. Vasculature structure analysis is an interesting and effective method for disease detection and analysis. In this work, a gradient-based blood vessel segmentation technique is proposed to assist retinal image analysis and to extract the retinal vessels. Edge detection is considered one of the major steps in the present work to characterize the boundaries. Itis used to reduce the unusual information and to preserve the necessary structural information. Various filters are constructed to gradient computation and edge detection. In the current work, a new method along with a new filter (kernel) has been proposed to detect edges efficiently. The results are compared with some well-known kernels. The proposed approach achieved Pratt Score 99.1536 value, which outperformed the corresponding values obtained by the other standard edge detectors, namely Sobel, Prewitt, Canny, and Robert's.

Cellular image processing using morphological analysis

Abstract: Cellular image analysis is considered one of the important job in biomedical image analysis. Analysis of cellular images obtained using a microscope is necessary in various disciplines including engineering and medical imaging. Cell detection is necessary in various jobs of microscopic analysis that helps physicians to diagnose and extract features. Accurate identification of cells is necessary for precise diagnosis. Analysis methods based on morphology is one of the major research area and also useful in biomedical image analysis as well as in bioinformatics. Morphology based analysis acts as the helping hand for physicians. Morphology based analysis methods are useful in determining cell shape, irregularity, feature extraction and classification. In this work, some of the methods have been reported which can be helpful in analyzing some practical problem by employing a suitable technique.

Automated Breast Cancer Identification by analyzing Histology Slides using Metaheuristic Supported Supervised Classification coupled with Bag-of-Features

Abstract: Breast cancer is one of the major threats to the human being. Early identification can prevent some of the premature deaths. Manual methods are sometimes very tedious and time consuming. Moreover manual diagnosis can be prone to error. Automated analysis can reduce the overhead of the manual diagnosis and reduce the error. In this work, an automated computer assisted framework has been proposed to analyze and detect the type of the disease from the current condition of the breast. Histological slides have been used for automated diagnosis. SIFT based feature selection and extraction method has been used followed by a Bag-of-Features method. The extracted features are classified by a metaheuristic supported Artificial Neural Network. Three models have been compared in terms of accuracy and obtained results are reported in a comprehensive manner.