The Importance Of Image Analysis

Texture is one of the crucial primitives in human vision and texture features have been used to identify contents of images. Examples are identifying crop fields and mountains from aerial image domain. Moreover, texture can be used to describe contents of images, such as clouds, bricks, hair, etc. Both identifying and describing characteristics of texture are accelerated when texture is integrated with color, hence the details of the important features of image objects for human vision can be provided. One crucial distinction between color and texture features is that color is a point, or pixel, property, whereas texture is a local-neighborhood property. The main motivation for using texture is the identifying and describing

Neurodegenerative diseases causes a wide variety of mental symptoms whose evolution is not directly related to the analysis made by radiologists on basis of images, who can hardly quantify systematic differences. This paper presents a new automatic (Based on software program) image analysis method that reveals different brain patterns associated to the presence of neurodegenerative diseases, finding systematic differences and therefore grading objectively any neurological disorder. An accurate solution can be provided by using Alzheimer’s diseases based on saliency map characterization is carried out on database images. This paper gives automatic image analysis method and attempts an approach for classification of brain images to search for pathology and normality part of brain by extracting salient features of input brain image and the region of interest is identified using kernel k-means algorithm. A support vector machine (SVM) a supervised learning process is used for classification of AD, which is recognized on basis of blue color is normal brain part and red color is pathology related.

For the detection hemorrhage in diabetic retinopathy image processing used. In this we devolepedautomated method for easier detection of hemorrhage in diabetic retinopathy.In this preprocessing process used for enhancing the contrast between background and object. For extraction of hemorrhage in fundus image after preprocessing template maching technique used.in which template of various shapes are used for detection of hemorrhage.for extract the proper shape of hemorrhage region growing segmentation technique

Abstract: Incidence rate of skin cancer are increasing day by day. Skin cancer is one of the deadliest forms of cancer but detected earlier can save the life time of the human being. An automated screening system is introduced to identify the presence of skin cancer in advance. In this paper, texture distinctiveness lesion segmentation algorithm is used. Experience and training-based characteristics of back propagation neural network is used with texture distinctiveness lesion segmentation algorithm, for identifying the normal and abnormal portions of skin .The most commonly occurring skin cancers are Melanoma, Basal and squamous cell carcinoma and actinic keratosis. The proposed system is to diagnose the presence of these skin cancers with high segmentation accuracy.

Statistical operations provide the means of principle of solving the many type of problems which require the uncertain information in cirrhosis. This paper discusses the statistical operations. Computed Tomography, Magnetic Resonance Imaging, Ultrasound etc has been proved very helpful in diagnosing liver cirrhosis. Cirrhosis is an endemic disease across the world that leads to observed

Machine vision, as a novel non-destructive analyzing technology, has been widely studied and employed in various industries (Davies, 2012). It can be applied to inspect raw materials or products during manufacturing (Davies, 2013). Recently the applications of machine vision in food industry are increasing rapidly since the developments in camera technology and the processing power of computer hardware (Chen et al., 2013). Machine vision technology has many advantages such as improving quality, increasing throughput, reducing waste, and reducing labor (Lind and Murhed, 2012). Generally, machine vision system includes image acquisition section and image processing & statistical pattern recognition section (Davies, 2013). The images processed by machine vision system could be grey-scale images, binary images, and conventional images took by ‘normal’ camera; it also could be images acquired from hyperspectral imaging, magnetic imaging, X-ray imaging, ultrasound imaging, thermal imaging, and fluorescence imaging

We have intentionally separated thresholding technique from region based due the usage of histogram and its simplicity. A lot of limitations are faced while applying segmentation namely:

Features are essential for any classification or analysis in image processing. There are many types of features which can be extracted from the images each gives its identical informations about the image. Here MA region supposed with properties such as shape, color and size which appears as a dark red colored circle shape. To identify MA and non- MA region feature vectors are formed for each candidate regions.

The existing classification methods have limitation in accuracy, exactness and require manual interaction. So, designing automated system using image segmentation techniques helps make the detection accurate and efficient.

Various classification methods have been developed to extract information from imageries. The two main types are pixel and object-based methods. Pixel-based methods can be cluster based unsupervised or supervised classification whereas the later uses statistical (e.g., maximum likelihood algorism) and non-statistical algorithms (e.g., support vector machines) (Lu, Li, Kuang, & Moran, 2014). The object-based classification which overcomes some of the particular problems encountered with pixel-based classification (Blaschke, 2010) was used to analyze

Feature plays a very important role in the area of image processing. Different feature extraction

This representation of Haar-Like Feature was used to find and select features according to pixel intensities and not by pixels values. Haar-like feature can be calculated using the scalar product between the input image and some Haar-like templates [14].

In pattern recognition and image processing, feature extraction is a special form of dimensionality reduction. The main goal of feature extraction is to obtain the most relevant information from the original data and represent that information in a lower dimensional space. Effective feature extraction from various intensity or color in images have been an important topic

Abstract - In research of area of agriculture, automatic plant disease recognition is important research topic. It may prove aids in monitoring huge arenas of crops, and thus unsurprisingly detect symptoms of disease as soon as they appear on plant leaves, stem. The term disease is usually used only for ruin of live plants. This research provides different methods used to study of leaf disease detection by using image processing technique.

Segmenting or dividing a digital image into region of interests or meaningful structures in general plays a momentous role in quite a few image processing tasks. Image analysis, image visualization, object representation are some of them. The prime objective of segmenting a digital image is to change its representation so that it looks more expressive for image analysis. During the course of action in image segmentation, each and every pixel of the image segmentation is assigned a label or value. The pixels that share the same value also share homogeneous traits. The examples can include color, texture, intensity or some other features. Image segmentation can be defined as the technique to divide the an image f (x, y) into a non empty subset f1, f2, ...., fn which is continuous and disconnected. This step contributes in feature extraction. There are quite a few applications where image segmentation plays a pivotal role. These applications vary from image filtering, face recognition, medical imaging