International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

In areas such as compuer vision and image processing,image thresholding has been and still is a relevant research area due to its wide spread usage and application.This paper try to undertake on image thresholding techniques on human DNA. The DNA image is used to identify the genetic disorderness. The microeletronic scope captured the uncleared image and it is too difficult to understand. This work try to analysis the content of DNA image. Medical image processing techniques are apply on DNA image. Variuos preprocessing techniques are performed like median filter, wiener filter, contrast stretching and riddler’s modified thresholding algorithm for thresholding the DNA image. In this paper proposed riddler thresholding modified algorithm to find the solution for median value using metrics. In future, the work done by this paper helps to do the segmentation on human DNA images

ABSTRACT This paper proposes an improved bilateral filter, adaptive Bilateral Filter, Switched Bilateral Filter to remove Gaussian noise from gray images. All the techniques are implemented using simulation in MATLABit is found that the standard BF is the best technique to remove Gaussian noise from images with high PSNR value. This technique is implemented in MATLAB-13 and various performance metrics taken into consideration are: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Normalized Color Difference (NCD) a good picture quality of de-noising images. The result shows that the proposed technique gives the best results than all other techniques in terms of all comparison parameters.

Run length encoding (RLE) is the method that allows the data compression for information in which pixels are replaced constantly. This paper examines the performance of the RLE algorithm. To compression the image is evaluate and compared. Medical image compression techniques irrelevance and redundancy of the image data in order to be able to store or transmit data in an efficient form. It’s useful process to save a lot of space and resources while sending images from one place to another.

Image security is a relatively very young and fast growing. Security of data or information is very important now a day in this world. Information security is most important for the business industries. Embedding information so that it cannot be visually perceived. Embedding information in digital data so that it cannot be visually or audibly perceived. In this paper we review some of the digital image watermarking and techniques and then DFT algorithm is also proposed. In this paper we review the robustness and metrics.

Image security is a relatively very young and fast growing. Security of data or information is very important now a day in this world. In this paper proposed to advantages and that working functionalities. This algorithm is verified on different watermarking images. And it's provide robust and secure results. To measure the effectiveness of this algorithm is provide embedding and extracting images. PSNR and MSE also calculated the embedding watermarking images. In this DWT watermarking embedding result images provide the good, secure and robust. In this paper proposed to how to process LSB technique.

Bioinformatics is a computer-assisted interface discipline dealing with the acquisition, storage, management, access and processing of molecular biology data.It is an interdisciplinary scientific tool without barriers among various disciplines of science like biology, mathematics, computer science and information technology.Bioinformatics has become an important part of many areas of biology.Bioinformatics tools aid in the comparison of genetic and genomic data and more generally in the understanding of evolutionary aspects of molecular biology.In structural biology, it aids in the simulation and modelling of DNA, RNA and Protein structures as well as molecular interactions.The most important feature of DNA is that it is usually composed of two polynucleotide chains twisted around each other in the form of a double helix.In this paper we have easily identified the complicated structure of DNA images for using several techniques of edge detection in image processing .weconsider various well known Algorithms and metrics used in image processing applied to DNA images in this comparison .

Mathematical morphology is an implement for extracting image components that are helpful in the demonstration and explanation of region shape, boundaries, skeletons, and convex hull. Morphological operations are used in pre and post processing for thinning, filtering. Morphology is a development method used to extract image meaningful. In this paper, study the different morphology methods that using in Gray Scale and Binary Dilation and Erosion. Keywords— Image processing, Medical image, Enhancement, Mathematical Morphology, Histogram.

Magnetic resonance imaging (MRI) of the brain is a precious tool to facilitate physician’s diagnoses and care for a mixture of brain diseases including stroke, cancer, and epilepsy. It presents exact information to discover the diseases. Histogram equalization is one of the important steps in image enhancement technique for MRI. This paper compares different methods like Brightness Preserving Bi-Histogram Equalization (BPBHE), Recursive Mean Separated Histogram Equalization (RMSHE), Brightness Preserving Dynamic Histogram Equalization (BPDHE), Dualistic Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-HE Method (MMBEBHE), using different objective quality metrics for MRI brain image Enhancement.

Medical image processing plays an essential role in providing information in wide area for such advanced images. Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. MRI of the brain is an invaluable tool to help physicians to diagnose and treat various brain diseases including stroke, cancer, and epilepsy. The specific information to evaluate the diseases. Histogram equalization is one of the important steps in image enhancement technique for MRI. There are several methods of image enhancement and each of them is needed for a different type of analysis. In this paper study and compare different Techniques like Global Histogram Equalization (GHE), Local histogram equalization (LHE), Brightness preserving Dynamic Histogram equalization (BPDHE) and Adaptive Histogram Equalization (AHE) using different objective quality measures for MRI brain image Enhancement.

Granular computing becomes known as an innovative multidisciplinary study and has established much attention in recent years. The framework shared multiple views and multiple levels of understanding in each view from many fields. The three components of the theory are labeled as the philosophy, methodology and the computation, the integration of the above view of granular computing as a way of structured thinking, structured problem solving and information processing and hierarchical granular structures. By using the levels of granularity, granular computing provides a systematic, natural way to analyze, understand, represent, and solve real world problems. The granular computing is a more philosophical way of thinking and a practical methodology of problem solving. This paper presents the study of basic inspiration for granulation and direct Granular computing as a structured combination of algorithmic and non-algorithmic information processing.

Fuzzy image processing is a powerful tool formulation of expert knowledge edge and the combination of imprecise information from different sources. The fuzzy technique is an operator in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective evaluation. Edge is a basic feature of image. The image edges include rich information that is very significant for obtaining the image characteristic by object recognition. Edge detection is the most commonly used technique in image processing. In this paper, the main aim is to study the theory of edge detection for dental x-ray image segmentation using fuzzy logic approach. Keywords— fuzzy logic, edge detection, dental x-ray image, image segmentation.

In the Digital Image Processing, edge detection playing a vital role. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. Artificial Neural Network, inspired by the way of biological nervous systems such as human brains process information and it is an information processing system which contains a large number of highly interconnected processing neurons. These neurons work together in a distributed manner to learn from the input information, to coordinate internal processing, and to optimize its final output. In this paper, the main aim is to study the theory of edge detection for dental x-ray image segmentation using a neural network approach. Keywordsedge detection, image segmentation, neural network, dental x-ray image.

Genetic Algorithm is an optimization solver, which does an analogy to Darwin evolution by combining mutation, crossover and selection step. One of the biggest advantages of Genetic Algorithm is its ability to find a global optimum. The X-ray data set, which consists of an image and its expected edge features, is used for training by the GA. Image edge detection refers to the extraction of the edges in a digital image. An edge is a boundary between the object and its background. Edge detection is most common approach to detect discontinuity in an image. Edge detection is a process to identify points in an image where discontinuities or sharp changes in intensity occur. This process is crucial to understanding the content of an image and has its applications in image analysis and machine vision. Edge detection is usually applied in initial stages of computer vision applications. In this paper, the main aim is to study the edge detection method for Dental X-ray image segmentation based on a genetic algorithm approach.

Neural Networks are based on the parallel architecture and inspired from human brains. Neural networks are a form of multiprocessor computer system, with simple processing elements, a high degree of interconnection, simple scalar messages and adaptive interaction between elements. One such application is image compression. Image compression is a process which minimizes the size of an image file without degrading the quality of the image to an unacceptable level. It also reduces the time required for images to be sent over the internet or downloaded from web pages. This paper proposes an Improved Backpropagation Neural Network Technique, for lossless image compression. The system also proves that the improved Backpropagation Neural Network Technique works better than the existing Huffman Coding Technique for lossless image compression by considering X-Ray images based on three metrics such as compression ratio, transmission time and compression performance. Experimental results are presented and compared.

The result of image thresholding is not always satisfactory due to the disturbing factors like vagueness, non-uniform illumination etc and to overcome these problems recently various researchers have proposed fuzzy image thresholding. The linear index of fuzziness for type-1 fuzzy sets by Zenzo et. al. and measure of ultrafuzziness for type-2 fuzzy sets by Tizhoosh has difficulties in handling MRI brain images with one level of gray value as background and other two levels of grayness as white matter and gray matter. Hence this paper proposes new modified thresholding measures for MRI brain images using type-1 and type-2 fuzzy sets. The results show the effectiveness of the proposed modified thresholding measures.

Medical image segmentation is a complex and challenging task due to the intrinsic nature of the images. The brain has particularly complicated structure and its precise segmentation is very important for detecting tumors, edema, and necrotic tissues, in order to prescribe appropriate therapy. Recently, rough sets and fuzzy sets has proved its soundness and usefulness in many medical applications including image segmentation. This paper presents a hybrid method that combines the granular rough set approach for brain image segmentation and fuzzy thresholding for brain white matter separation and the results show the effectiveness of the method.

Rough set is approximate representation of a crisp set. Rough set theory provides an approach to approximation of sets that leads to useful forms of granular computing. Several applications have revealed the need to extend the traditional rough set approach. A special place among various extensions is taken by the approach that replaces the relation based on equivalence with a tolerance relation. Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as feature identification, dimensionality reduction, pattern classification and image segmentation. In this paper, the main aim is to survey the Rough set Theory for medical image segmentation. Index Terms—Rough Set Theory, Medical Image Segmentation, Granular computing.

Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Segmentation is an essential step in image processing since it conditions the quality of the resulting interpretation. Lots of approaches have been proposed and a dense literature is available In order to extract as much information as possible from an environment, multicomponent images can be used. In the last decade, multicomponent images segmentation has received a great deal of attention for soft computing applications because it significantly improves the discrimination and the recognition capabilities compared with gray-level image segmentation methods. In this paper, the main aim is to understand the soft computing approach to image segmentation.