Biomedical image analysis methods are gradually shifting towards computer-aided solutions from manual investigations to save time and improve the quality of the diagnosis. Deep learning-assisted biomedical image analysis is one of the major and active research areas. Several researchers are working in this domain because deep learning-assisted computer-aided diagnostic solutions are well known for their efficiency. In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented. This chapter can be helpful for the researchers to understand the recent developments and drawbacks of the present systems. The discussion is made from the perspective of the computer vision, pattern recognition, and artificial intelligence. This chapter can help to get future research directions to exploit the blessings of deep learning techniques for biomedical image analysis.

An Overview of Biomedical Image Analysis From the Deep Learning Perspective

Biomedical image analysis is one of the most challenging and inevitable part of the computer aided diagnostic systems. Automated analysis of the image can detect various diseases automatically without human intervention. Computer vision and artificial intelligence can sometimes defeat human diagnostic power and can reveal some hidden information from the biomedical images. In the field of health care, accurate results are highly required within stipulated amount of time. But to increase accuracy, proper preprocessing with sophisticated algorithms is required. Low quality image can affect processing algorithm which can leads to the poor result. Therefore, sophisticated preprocessing methods are required to get reliable results. Contrast is one of the most important parameter for any image. Poor contrast may cause several problems for computer vision algorithms. Conventional algorithms for contrast adjustment may not be suitable for many purposes. Sometimes, these methods can generate some images that may lose some critical information. In this work, a contrast optimization method based on well-known metaheuristic technique called genetic algorithm with elitism is used that can enhance the biomedical images for better analysis. A new kernel has been proposed to detect the edges. Obtained results illustrate the efficiency of the proposed algorithm.

Contrast Optimization using Elitist Metaheuristic Optimization and Gradient Approximation for Biomedical Image Enhancement

Image segmentation has been an active topic of research for many years. Edges characterize boundaries, and therefore, detection of edges is a problem of fundamental importance in image processing. Edge detection in images significantly reduces the amount of data and filters out useless information while preserving the important structural properties in an image. Edges carry significant information about the image structure and shape, which is useful in various applications related with computer vision. In many applications, the edge detection is used as a pre-processing step. Edge detection is highly beneficial in automated cell counting, structural analysis of the image, automated object detection, shape analysis, optical character recognition, etc. Different filters are developed to find the gradients and detect edges. In this chapter, a new filter (kernel) is proposed, and the compass operator is applied on it to detect edges more efficiently. The results are compared with some of the previously proposed filters both qualitatively and quantitatively.

An Advanced Approach to Detect Edges of Digital Images for Image Segmentation

Breast cancer is one of the major threats to the human being. Early identification can prevent some of the premature deaths. Manual methods are sometimes very tedious and time consuming. Moreover manual diagnosis can be prone to error. Automated analysis can reduce the overhead of the manual diagnosis and reduce the error. In this work, an automated computer assisted framework has been proposed to analyze and detect the type of the disease from the current condition of the breast. Histological slides have been used for automated diagnosis. SIFT based feature selection and extraction method has been used followed by a Bag-of-Features method. The extracted features are classified by a metaheuristic supported Artificial Neural Network. Three models have been compared in terms of accuracy and obtained results are reported in a comprehensive manner.

Automated Breast Cancer Identification by analyzing Histology Slides using Metaheuristic Supported Supervised Classification coupled with Bag-of-Features

Computer aided diagnostic is one of the most active research area and has huge impact on the health care industry. With the advent of intelligent methods, biomedical data processing becomes easier and less error prone. Moreover, remote health care is also possible using the IoT infrastructure. However, data security over the network is always considered as a challenge. Biomedical data are generally sensitive to external disturbances and small manipulation in the data may cause huge difference in the ultimate result. Wrong diagnosis can be life threatening in some scenarios or can be severe in almost every instance. Therefore, biomedical data security is one of the major challenge and necessary for remote health care. In this work, some application of cryptographic methods have been discussed which are used to protect biomedical images from unwanted modifications and access. This work can be highly beneficial in future research in the field of biomedical image protection as well as multimedia data security.

A Study on the Applications of the Biomedical Image Encryption Methods for Secured Computer Aided Diagnostics

The current work proposes a neural based detection method of two different skin diseases using skin imaging. Skin images of two diseases namely Basel Cell Carcinoma and Skin Angioma are utilized. SIFT feature extractor has been employed followed by a clustering phase on feature space in order to reduce the number of features suitable for neural based models. The extracted bag-of-features modified dataset is used to train metaheuristic supported hybrid Artificial Neural Networks to classify the skin images in order to detect the diseases under study. A well-known multi objective optimization technique called Non-dominated Sorting Genetic Algorithm — II is used to train the ANN (NN-NSGA-II). The proposed model is further compared with two other well-known metaheuristic based classifier namely NN-PSO (ANN trained with PSO) and NN-CS (ANN trained with Cuckoo Search) in terms of testing phase confusion matrix based performance measuring metrics such as accuracy, precision, recall and F-measure. Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model.

Image based skin disease detection using hybrid neural network coupled bag-of-features

The Ultra Wide Band (UWB)radio technology has attracted much interest in academics, industrial and standardization activities. Transmission systems with instantaneous spectral occupancy of higher fractional bandwidth is characterized by UWB. The antenna is probably the most overlooked part of a RF (Radio Frequency) design. The range, performance, and legality of a RF link are critically dependent upon the antenna. The radio spectrum is the source that is needed to be utilized using cognitive radio (CR) system. Detecting primary users (PU) over the spectrum is one of the problems that we face in the cognitive radio system. Here the UHF (Ultra High Frequency) TV Band is specifically addressed which is a target band of IEEE802.22 standardization. The UHF TV band is 336MHz wider according to CCIR (Consultative Committee on International Radio) standards. One major challenge in designing a UWB antenna for UHF band is limiting the physical size of the antenna. The paper illustrates the successful design, implementation and testing of a UWB antenna for cognitive radios in the UHF TV Band. The antenna is simple, cheap and very compact and can be used in fixed transceivers. Different papers on various configurations of wideband antenna have been studied and the different antenna parameters have been verified. The microstrip antenna has advantages of being low profile, light in weight and cheap. In this paper we have designed and simulated a GPS micro strip antenna that perform wide band CP radiation. The proposed antenna consists of orbicular micro strip patch, a substrate of feeding network and a circular patch above and orbicular patch antenna fed by two coaxial wires that is considered as the base structure. The feeding network was designed and combined with the antenna. The proposed CP antenna can be designed by adjusting the dimensions of the inner and the outer radius of the orbicular patch to determine the operating frequency of the antenna. Then the parameters of the branch line directional coupler can be fine tuned to achieve the required frequency with good impedance matching.

Comparative study of different techniques to design ultra wideband antenna in cognitive radio

Cognitive Radio, Software Defined radio Spectrum Sensing represents three development areas which are changing the world of Wireless Communication. The objective of this paper is to implement a new Hard and Soft detector based spectrum sensing method in a real time transmission network and optimize it. In particular, the aim is to detect the signals in the air, and estimate the power spectral density of the sources emitting these signals through collaborations of the receiver nodes. To achieve this, a receiver network based cooperative wideband spectrum sensing scheme is implemented using real time Transmission and receiving system using MATLAB and WARP environment. Two different types of Hard and Soft detection techniques have been compared with a new Softened Hard Detection scheme to show that the new method increases efficiency.

Abhishek Bhattacharya

Papers

Image based skin disease detection using hybrid neural network coupled bag-of-features

Comparative study of different techniques to design ultra wideband antenna in cognitive radio

Performance optimization of a soft and hard detector in cognitive radio environment using WARP