In this paper, our goal is to determine the boundaries of lesion and then calculate the area of existing lesion in breast magnetic resonance (MR) images to provide a useful information to the radiologists. For this purpose, at first stage region growing (RG) method and active contour model (Snake) is applied to the images to make the boundaries of lesion visible.  
RG method is one of the simplest approaches for image segmentation and provides accurate results with lower computation time due to its seed point initialization step. Snake method molds a closed contour to the boundary of a region in an image and is also popular in medical image segmentation studies. In the presented study, both of these methods are utilized to determine the lesion boundaries. 
After determining the boundaries of lesion accurately in the second stage of the study, bit-quad method is applied to the segmented images. Bit quad method is used to compute the area and perimeter of binary lesion images based on matching the logical state of regions of image to binary patterns. Finally, to evaluate the performance of the proposed study, computer simulations are performed. It is demonstrated via computer simulations that the lesion area and parameter values are very close to real values. By means of this study it is aimed to support radiologists during diagnosis and assessment of breast lesions.

Plant is a gift of almighty to the living being in the earth. Leaf is an essential component for any types of plant including crops, fruit and vegetables. Before the scheduled decay of the leaf due to deficiency there are patches of dead zone spot or sections generally visible. This paper introduces a novel image based analysis to identify patches of dead zone spot or sections generally visible due to deficiency. Clustering, colour object based segmentation and colour transformation techniques using significant salient features identification are applied over 12 plant leaves collected naturally from gardens and crop fields. Hue, saturation and Value based and L*a*b* colour model based object analysis is being applied over diseased leaf and portion of leaf to identify the dead zone automatically. Derivative based edge analysis is being applied to identify the outline edge and dead zone segmentation in leaf image. K-means clustering has played an important role to cluster dead zone using colour based object area segmentation.

In Modern Radio Interferometry Various Techniques have been developed for the Reconstruction of the high-dimensional Data scalability Radio Images. CLEAN Variants are widely used in Radio Astronomy because of its computationally efficiency and easiness to understand. CLEAN deconvolves different polarization component images independently and nonlinearly from the point source response by removing the dirty beam pattern form the images. CLEAN Algorithms have been evaluated in this paper for both single field "Deconvolution" (Hogbom, Clark, Clark Stokes, and Cotton Schwab) and multi-field "Deconvolution" (Multi Scale, Multi Frequency and Multi Scale Multi frequency). Based upon simulation results,  it is clear that more updated techniques are needed for Large radio telescopes to face big data, extended sources emissions and fast imaging issues which are using dimensionality reduction from the perspective of the compressed sensing theory and to study its interplay with imaging algorithms which are designed in the context of convex optimization combined with sparse representations. 

Texture classification is widely employed in many computer vision and pattern recognition applications. Texture classification is performed in two phases namely feature extraction and classification. Several feature extraction methods and feature descriptors have been proposed and local binary pattern (LBP) has attained much attraction due to their simplicity and ease of computation. Several variants of LBP have been proposed in literature. This paper presents a performance evaluation of LBP based feature descriptors namely LBP, uniform LBP (ULBP), LBP variance (LBPV), LBP Fourier histogram, rotated LBP (RLBP) and dominant rotation invariant LBP (DRLBP). For performance evaluation, nearest neighbor classifier is employed. The benchmark OUTEX texture database is used for performance evaluation in terms of classification accuracy and runtime.

Human brain signals obtained by the human brain sensor electrodes measure the cerebral activities on the human brain. The main aim of our research is to improve the human brain activities based on the human brain signal. The entire procedure contains three steps. The first step is to acquire the brain signal, then develop this brain signal with the proposed method and finally improve the human brain activities with this modified brain signal. The entire procedure will proceed in a proposed Neuroheadset device embedded with necessary sensors using the non-invasive technique. This device will help to acquire the brain signal, modify this signal and improve the brain activities with this modified brain signal. In this research, we illustrated the first two steps like signal acquisition and signal modification. In the experiment, we used Electroencephalogram as an efficient non-invasive signal acquisition technique for acquiring the brain signal and also introduced a proposed method to modify this signal. This method helped to improve the human brain signal using the required times of the iteration process. In the experiment level, several iteration processes have been done to get above 90% improvement rate of the brainwaves. In this research, the improved signal has been considered based on the generated brain signal in various aspects like human intelligence, memory and also the capability of better feelings.

With the rapid development of science and technology, intelligent systems have been applied to various fields. A monitoring system for the operating status of loudspeakers based on power line communication was designed and implemented. In this paper, firstly analyzes the deficiencies of previous research, and then according to the actual situation, it is concluded that the power line communication technology is more suitable for loudspeaker operating status monitoring than other communication technologies. The overall design, hardware design and software design of the entire system was introduced. And in the last, the reliability of the system were proved by many experiments. This system can be used in other applications in addition to the monitoring of the operating status of the loudspeakers.

Image segmentation plays the significant roles in image processing, computer vision and as well as in pattern recognition. The Segmentation process subdivides an image into its constituent parts or objects, such that level of subdivision depends on the problem to be solved. The aim of image segmentation is partitioning an image within homogeneous regions that are significantly meaningful concerning some characteristics like intensity or texture. Based on clustering, a large number of researches have been done in the area of image segmentation. This paper presents an efficient image segmentation method in which the self organizing feature map (SOFM) is used for initial segmentation. After the initial segmentation, the segmented image is used by the K-means algorithm for further segmentation. Finally, the procedures for automatic splitting and merging the cluster are applied to obtain the appropriate number of segments in segmented image and as well as better segmented results. For analyzing the performance, we calculate the statistical measure named as Davies-Bouldin index (DB-index). The observation shows that, this method gives the better segmented results compared with K-Means algorithm, linear discriminant analysis (LDA) and K-Means based image segmentation method and also SOFM and K-Means based image segmentation approach.