Evaluation of Ischemic Stroke Region From CT/MR Images Using Hybrid Image Processing Techniques

An ischemic stroke (IS) naturally originates with rapid onset neurological shortfall, which can be verified by analyzing the internal regions of brain. Computed tomography (CT) and magnetic resonance image (MRI) are the commonly used non-invasive medical examination techniques used to record the brain abnormalities for clinical study. In order to have a pre-opinion regarding the brain abnormality in clinical level, it is essential to use a suitable image processing tool to appraise the digital CT/MR images. In this chapter, a hybrid image processing technique based on the social group optimization assisted Tsallis entropy and watershed segmentation (WS) is proposed to examine ischemic stroke region from digital CT/MR images. For the experimental study, the digital CT/MRI datasets like Radiopedia, BRATS-2013, and ISLES-2015 are considered. Experimental result of this study confirms that, proposed hybrid approach offers superior results on the considered image datasets.

Verification of Multimodal Biometric

Authenticating the identity of an individual has become an important aspect of many organizations. The reasons being to secure authentication process, to perform automated attendance, or to provide bill payments. This need of providing automated authentication has led to concerns in the security and robustness of such biometric systems. Currently, many biometric systems that are organizations are unimodal, which means that use single physical trait to perform authentication. But, these unimodal systems suffer from many drawbacks. These drawbacks can be overcome by designing multimodal systems which use multiple physical traits to perform authentication. They increase reliability and robustness of the systems. In this chapter, analysis and comparison of multimodal biometric systems is proposed for three physical traits like iris, finger, and palm. All these traits are treated independently, and feature of these traits are extracted using two algorithms separately.

Visual Saliency and Perceptual Quality Assessment of 3D Meshes

This chapter concerns the visual saliency and the perceptual quality assessment of 3D meshes. Firstly, the chapter proposes a definition of visual saliency and describes the state-of-the-art methods for its detection on 3D mesh surfaces. A focus is made on a recent model of visual saliency detection for 3D colored and non-colored meshes whose results are compared with a ground-truth saliency as well as with the literature's methods. Since this model is able to estimate the visual saliency on 3D colored meshes, named colorimetric saliency, a description of the construction of a 3D colored mesh database that was used to assess its relevance is presented. The authors also describe three applications of the detailed model that respond to the problems of viewpoint selection, adaptive simplification and adaptive smoothing. Secondly, two perceptual quality assessment metrics for 3D non-colored meshes are described, analyzed, and compared with the state-of-the-art approaches.

An Intelligent-Based Wavelet Classifier for Accurate Prediction of Breast Cancer

The detection of cancer in the breast is done using mammograms (x-ray images). The authors propose a CAD framework for distinguishing little changes in mammogram which may demonstrate malignancies which are too little to be felt either by the lady herself or by a radiologist. In this chapter, they build up a framework for analysis, visualization, and prediction of cancer in breast tissue by utilizing Intelligent based wavelet classifier. Intelligent-based wavelet classifier is a new approach constructed using texture value and wavelet neural network. The proposed framework is applied to the genuine clinical database of 160 mammograms gathered from mammogram screening focuses. The execution of the CAD framework is examined utilizing ROC curve. This will help the specialists in determination of the breast tissues either cancerous or noncancerous in an accurate way.

Underwater Image Segmentation Using Human Psycho Visual Phenomenon

In this chapter, a novel method is proposed for underwater image segmentation based on human psycho visual phenomenon (HVS). In the proposed method the texture property of an image is captured by decomposing it into frequency sub-bands using M-band wavelet packet transform. The sub-bands represent the image in different scales and orientations. The large numbers of sub-bands are pruned by an adaptive basis selection. The proper sub-bands for segmentation are selected depending on the HVS. The HVS imitates the original visual technique of a human being and it is used to divide each sub-band in Weber, De-Vries Rose, and saturation regions. A wavelet packet sub-band is selected for segmentation depending on those three regions. The performance of the proposed method is found to be superior to that of the state-of-the-art methods for underwater image segmentation on standard data set.

3D Reconstruction Algorithms Survey

Over the past years, 3D reconstruction has proved to be a challenge. With augmented reality and robotics attracting more attention, the demand for efficient 3D reconstruction algorithms has increased. 3D reconstruction presents a problem in computer vision and as a result, much work has been dedicated to solving it. Different design choices were made to consider different components of the process. Examples of these differences are how the scanning process is tackled, how the 3D reconstructed world is represented, among other aspects. Therefore, an evaluation of these algorithms is necessary. This chapter focuses on the properties that facilitate the evaluation of 3D reconstruction algorithms and provides an evaluation of the various algorithms.

3D Medical Images Compression

Medical images are digital representations of the body. Medical imaging technology has improved tremendously in the past few decades. The amount of diagnostic data produced in a medical image is vast and as a result could create problems when sending the medical data through a network. To overcome this, there is a great need for the compression of medical images for communication and storage purposes. This chapter contains an introduction to compression types, an overview of medical image modalities, and a survey on coding techniques that deal with 3D medical image compression.

Proper Enhancement and Segmentation of the Overexposed Color Skin Cancer Image

Proper enhancement and segmentation of the overexposed color skin cancer images is a great challenging task in medical image processing field. Computer-aided diagnosis (CAD) facilitates quantitative analysis of digital images with a high throughput processing rate. But, analysis of CAD purely depends on the input image quality. Therefore, in this study, overexposed and washed out skin cancer images are enhanced properly with the help of exact hue-saturation-intensity (eHSI) color model and contrast limited adaptive histogram equalization (CLAHE) method which is applied through this model. eHSI color model is hue preserving and gamut problem free. Any gray level image enhancement method can be easily employed for color image through this eHSI model. The segmentation of these enhanced color images has been done by employing one unsupervised clustering approach with the assistance of seven different gray level thresholding methods. Comparison of the segmentation efficiency of gray level thresholding methods has been done in the cases of overexposed as well as for enhanced images.

Multimodal Semantics and Affective Computing from Multimedia Content

Advancements in technologies and increasing popularities of social media websites have enabled people to view, create, and share user-generated content (UGC) on the web. This results in a huge amount of UGC (e.g., photos, videos, and texts) on the web. Since such content depicts ideas, opinions, and interests of users, it requires analyzing the content efficiently to provide personalized services to users. Thus, it necessitates determining semantics and sentiments information from UGC. Such information help in decision making, learning, and recommendations. Since this chapter is based on the intuition that semantics and sentiment information are exhibited by different representations of data, the effectiveness of multimodal techniques is shown in semantics and affective computing. This chapter describes several significant multimedia analytics problems such as multimedia summarization, tag-relevance computation, multimedia recommendation, and facilitating e-learning and their solutions.

Improving the Classification Accuracy of Multidimensional Overlapping Objects Based on Neuro-Fuzzy Analysis of Generalized Patterns

To improve the classification accuracy of multidimensional overlapping objects, a new hybrid neuro-fuzzy FCNN-SOM-FMLP network, combining the fuzzy cell neural network of Kohonen (FCNN-SOM) and the fuzzy multilayer perceptron (FMLP), and the algorithms for its training are proposed. This combination allows for clustering of generalized intersecting patterns (the extensional approach) and training the classification network basing on the identification of integrated pattern characteristics in the isolated clusters (intentional approach). The new FCNN-SOM-FMLP architecture features a high degree of self-organization of neurons, an ability to manage selectively individual neuronal connections (to solve the problem of “dead” neurons), the high flexibility, and the ease of implementation. The experimental results show the temporal efficiency of algorithms of self-organization and training and the improvement of the separating properties of the network in the case of overlapping clusters. Calculated technological and economic generalized values of countries.