Automatic Computerized Diagnostic Tool for Down Syndrome Detection in Fetus

Down syndrome is a genetic disorder and the chromosome abnormality observed in humans that can cause physical and mental abnormalities. It can never be cured or rectified. Instead it has to be identified in the fetus and prevented from being born. Many ultrasonographic markers like nuchal fold, nasal bone hypoplasia, femur length, and EIF are considered to be the symptoms of Down syndrome in the fetus. This chapter deals with the creation of automatic and computerized diagnostic tool for Down syndrome detection based on EIF. The proposed system consists of two phases: 1) training phase and 2) testing phase. In training phase, the fetal images with EIF and Down syndrome is analyzed and characteristics of EIF are collected. In testing phase, detection of Down syndrome is performed on the fetal image with EIF based on the knowledge cluster obtained using ESOM. The performance of the proposed system is analyzed in terms of sensitivity, accuracy, and specificity.

Medical Image Lossy Compression With LSTM Networks

Medical images have a larger size when compared to normal images. There arises a problem in the storage as well as in the transmission of a large number of medical images. Hence, there exists a need for compressing these images to reduce the size as much as possible and also to maintain a better quality. The authors propose a method for lossy image compression of a set of medical images which is based on Recurrent Neural Network (RNN). So, the proposed method produces images of variable compression rates to maintain the quality aspect and to preserve some of the important contents present in these images.

Histopathological Image Analysis in Medical Decision Making

Digital pathology is one of the significant methods in the medicine field to diagnose and treat cancer. The cell morphology and architecture distribution of biopsies are analyzed to diagnose the spread and severity of the disease. Manual analyses are time-consuming and subjected to intra- and inter-observer variability. Digital pathology and computer-aided analysis aids in enormous applications including nuclei detection, segmentation, and classification. The major challenges in nuclei segmentation are high variability in images due to differences in preparation of slides, heterogeneous structure, overlapping clusters, artifacts, and noise. The structure of the proposed chapter is as follows. First, an introduction about digital pathology and significance of digital pathology techniques in cancer diagnosis based on literature survey is given. Then, the method of classification of histopathological images using deep learning for different datasets is proposed with experimental results.

A Study on Segmentation of Leukocyte Image With Shannon's Entropy

In recent years, a considerable number of approaches have been proposed by the researchers to evaluate infectious diseases by examining the digital images of peripheral blood cell (PBC) recorded using microscopes. In this chapter, a semi-automated approach is proposed by integrating the Shannon's entropy (SE) thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures. This work implements a two-step practice with cuckoo search (CS) and SE-based pre-processing and DRLS-based post-processing procedure to examine the PBC pictures. During the experimentation, the PBC pictures are adopted from the database leukocyte images for segmentation and classification (LISC). The proposed approach is implemented by considering the RGB scale and gray scale version of the PBC pictures, and the performance of the proposed approach is confirmed by computing the picture similarity and statistical measures computed with the extracted stained blood cell with the ground truth image.

Adaptive Prediction Methods for Medical Image/Video compression for Telemedicine Application

Due to rapid development of multimedia communication and advancement of image acquisition process, there is a crucial requirement of high storage and compression techniques to mitigate high data rate with limited bandwidth scenario for telemedicine application. Lossless compression is one of the challenging tasks in applications like medical, space, and aerial imaging field. Apart from achieving high compression ratio, in these mentioned applications there is a need to maintain the original imaging quality along with fast and adequate processing. Predictive coding was introduced to remove spatial redundancy. The accuracy of predictive coding is based on the choice of effective and adaptive predictor which is responsible for removing spatial redundancy. Medical images like computed tomography (CT) and magnetic resonance imaging (MRI) consume huge storage and utilize maximum available bandwidth. To overcome these inherent challenges, the authors have reviewed various adaptive predictors and it has been compared with existing JPEG and JPEG LS-based linear prediction technique for medical images.

Analysis of Medical Images Using Fractal Geometry

Fractal dimension is an emerging research area in order to characterize the complex or irritated objects found in nature. These complex objects are failed to analyze by classical Euclidian geometry. The concept of FD has extensively applied in many areas of application in image processing. The thought of the FD will work based upon the theory of self-similarity because it holds structures that are nested with one another. Over the last years, fractal geometry was applied extensively in medical image analysis in order to detect cancer cells in human body because our vascular system, nervous system, bones, and breast tissue are so complex and irregular in pattern, and also successfully applied in ECG signal, brain imaging for tumor detection, trabeculation analysis, etc. In order to analyze these complex structures, most of the researchers are adopting the concept of fractal geometry by means of box counting technique. This chapter presents an overview of box counting and its improved algorithms and how they work and their application in the field of medical image processing.

HE Stain Image Segmentation Using an Innovative Type-2 Fuzzy Set-Based Approach

HE stain images are widely used in medical diagnosis and often considered a gold standard for histology and pathology laboratories. A proper analysis is needed to have a critical decision about the status of the diagnosis of the concerned patient. Segmentation is always considered as an advanced stage of image analysis where objects of similar properties are put in one segment. But segmentation of HE stain images is not an easy task as these images involve a high level of fuzziness with them mainly along the boundary edges. So, traditional techniques like hard clustering techniques are not suitable for segmenting these images. So, a new approach is proposed in this chapter to deal with this problem. The proposed approach is based on type-2 fuzzy set and is new. The experimental results prove the superiority of the proposed technique.

A Novel Approach of K-SVD-Based Algorithm for Image Denoising

In recent years, denoising has played an important role in medical image analysis. Image denoising is still accepted as a challenge for researchers and image application developers in medical image applications. The idea is to denoise a microscopic image through over-complete dictionary learning using a k-means algorithm and singular value decomposition (K-SVD) based on pursuit methods. This approach is good in performance on the quality improvement of the medical images, but it has low computational speed with high computational complexity. In view of the above limitations, this chapter proposes a novel strategy for denoising insight phenomena of the K-SVD algorithm. In addition, the authors utilize the technology of improved dictionary learning of the image patches using heap sort mechanism followed by dictionary updating process. The experimental results validate that the proposed approach successfully reduced noise levels on various test image datasets. This has been found to be more accurate than the best in class denoising approaches.

Multi-Criteria Decision-Making Techniques for Histopathological Image Classification

This chapter presents an overview of methods that have been proposed for analysis of histopathological images. Diagnosing and detecting abnormalities in medical images helps the pathologist in making better decisions. Different machine learning algorithms such as k-nearest neighbor, random forest, support vector machine, ensemble learning, multilayer perceptron, and convolutional neural network are incorporated for carrying out the analysis process. Further, multi-criteria decision-making (MCDM) methods such as SAW, WPM, and TOPSIS are used to improve the efficiency of the decision-making process.

Analysis of Color Image Encryption Using Multidimensional Bogdanov Map

Image encryption is a main concern in digital transmission of data over communication network. As encryption and decryption of image has got considerable attention in the past decades, its effectiveness and compatibility need to be taken care of. The work reported in this chapter is mainly concerned with enhancement of dimension in image encryption technique. The work mainly deals with pixels shuffling of an image using Bogdanov chaotic map for both gray and color image, where encryption and decryption process are associated with the key. In color image, the image is divided into all three planes (RGB) individually. Scrambling is done with all three planes individually. All the three planes are summed up into a single plane which gives us the final result. In Bogdanov map, old pixel position is replaced with new pixel position. Further, the authors analyzed security of image encryption techniques with two parameters called NPCR and UACI. The efficacy of the encryption process can be seen in experimental results.