Automatic Detection and Classification of Ischemic Stroke Using K-Means Clustering and Texture Features

Computed tomography images are widely used in the diagnosis of ischemic stroke because of its faster acquisition and compatibility with most life support devices. This chapter presents a new approach to automated detection of ischemic stroke using k-means clustering technique which separates the lesion region from healthy tissues and classification of ischemic stroke using texture features. The proposed method has five stages, pre-processing, tracing midline of the brain, extraction of texture features and feature selection, classification and segmentation. In the first stage noise is suppressed using a median filtering and skull bone components of the images are removed. In the second stage, midline shift of the brain is calculated. In the third stage, fourteen texture features are extracted and optimal features are selected using genetic algorithm. In the fourth stage, support vector machine, artificial neural network and decision tree classifiers have been used. Finally, the ischemic stroke region is extracted by using k-means clustering technique.

Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

Incorporation of Depth in Two Dimensional Video Captures

Stereoscopic vision in cameras is an interesting field of study. This type of vision is important in incorporation of depth in video images which is needed for the ability to measure distances of the object from the camera properly i.e. conversion of two dimensional video image into three dimensional video. In this chapter, some of the basic theoretical aspects of the methods for estimating depth in 2D video and the current state of research have been discussed. These methods are frequently used in the algorithms for estimating depth in the 2D to 3D video techniques. Some of the recent algorithms for incorporation depth in 2D video are also discussed and from the literature review a simple and generic system for incorporation depth in 2D video is presented.

Study of Noise Removal Techniques for Digital Images

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Biomedical Imaging Techniques

Biomedical imaging techniques had significantly improved the health care of patients. Image guided therapy has reduced the high risk of human errors with improved accuracy in disease detection and surgical procedures. The chapter provides an overview of existing imaging methods and current imaging approaches and their potential to unravel the challenges in medical field. First part of the chapter picture outs the basic concepts and mechanism of various imaging techniques that are currently in use. The second part explains about the features of image processing system and future trends in image guided therapy extended with a short discussion on radiation exposure in medical imaging. The authors trust the chapter to be beneficial to the beginners in the area of medical science and to the clinicians.

Iris Identification System

Recent research on iris is not only on recognition; emerging trends are also in medical diagnostics, personality identification. The iris based recognition system rely on patterns/textures present in the iris, the color of the iris, visible features present in the iris, geometric features of the iris and the SIFT features. An overview of biometric generation is presented. Human iris can be viewed as a multilayered structure in its anterior view. The iris consists of three zones, the pupillary zone, collarette and the ciliary zone. The texture features present in the pupillary zone and collarette are used for identification. As these features are closer to the pupil they are not affected by the occlusion caused by eyelid or eyelashes. The geometric features of the iris can also be used for human identification. The structure of the iris is more related to the geometric shape and hence the extraction of these features is also possible. An overview of the performance metrics to evaluate a biometric system is also presented.

Indexing of Image Features Using Quadtree

In Content based image retrieval (CBIR) applications, the idea of indexing is mapping the extracted descriptors from images into a high-dimensional space. In this paper, visual features like color, texture and shape are considered. The color features are extracted using color coherence vector (CCV), texture features are obtained from Segmentation based Fractal Texture Analysis (SFTA). The shape features of an image are extracted using the Fourier Descriptors (FD) which is the contour based feature extraction method. All features of an image are then combined. After combining the color, texture and shape features using appropriate weights, the quadtree is used for indexing the images. It is experimentally found that the proposed indexing method using quadtree gives better performance than the other existing indexing methods.

Image and Video Restoration and Enhancement via Sparse Representation

The chapter provides a survey of recent advances in image/video restoration and enhancement via spare representation. Images/videos usually unavoidably suffer from noises due to sensor imperfection or poor illumination. Numerous contributions have addressed this problem from diverse points of view. Recently, the use of sparse and redundant representations over learned dictionaries has become one specific approach. One goal here is to provide a survey of advances in image/video denoising via sparse representation. Moreover, to consider more general types of noise, this chapter also addresses the problems about removals of structured/unstructured components (e.g., rain streaks or blocking artifacts) from image/video. Moreover, image/video quality may be degraded from low-resolution due to low-cost acquisition. Hence, this chapter also provides a survey of recently advances in super-resolution via sparse representation. Finally, the conclusion can be drawn that sparse representation techniques have been reliable solutions in several problems of image/video restoration and enhancement.

Color Features and Color Spaces Applications to the Automatic Image Annotation

Nowadays images play a crucial role in different fields such as medicine, advertisement, education and entertainment. Describing images content and retrieving them are important fields in image processing. Automatic image annotation is a process which produces words from a digital image based on the content of this the image by using a computer. In this chapter, after an introduction to neighbor voting algorithm for image annotation, we discuss the applicability of color features and color spaces in automatic image annotation. We discuss the applicability of three color features (color histogram, color moment and color Autocorrelogram) and three color spaces (RGB, HSI and YCbCr) in image annotation. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that using different color spaces and color features helps to select the best color features and spaces in image annotation area.

Gait Based Biometric Authentication System with Reduced Search Space

Deployment of human gait in developing new tools for security enhancement has received growing attention in modern era. Since the efficiency of any algorithm depends on the size of search space, the aim is to propose a novel approach to reduce the search space. In order to achieve this, the database is split into two based on gender and the search is restricted in the identified gender database. Then highly discriminant gait features are selected by forward sequential feature selection algorithm in the confined space. Experimental results evaluated on the benchmark CASIA B gait dataset with the newly proposed combined classifier kNN-SVM, shows less False Acceptance Rate (FAR) and less False Rejection Rate (FRR).