Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

An Automated Modified Region Growing Technique for Prostate Segmentation in Trans-Rectal Ultrasound Images

Medical imaging plays a vital role in the medical field because it is widely used in diseases diagnosis and treatment of patients. There are different modalities of medical imaging such as ultrasounds, x-rays, Computed Tomography (CT), Magnetic Resonance (MR), and Positron Emission Tomography (PET). Most of these modalities usually suffer from noise and other sampling artifacts. The diagnosis process in these modalities depends mainly on the interpretation of the radiologists. Consequently, the diagnosis is subjective as it is based on the radiologist experience. Medical image segmentation is an important process in the field of image processing. It has a significant role in many applications such as diagnosis, therapy planning, and advanced surgeries. There are many segmentation techniques to be applied on medical images. However, most of these techniques are still depending on the experts, especially for initializing the segmentation process. The artifacts of images can affect the segmentation output. In this thesis, we propose a new approach for automatic prostate segmentation of Trans-Rectal UltraSound (TRUS) images by dealing with the speckle not as noise but as informative signals. The new approach is an automation of the conventional region growing technique. The proposed approach overcomes the requirement of manually selecting a seed point for initializing the segmentation process. In addition, the proposed approach depends on unique features such as the intensity and the spatial Euclidean distance to overcome the effect of the speckle noise of the images. The experimental results of the proposed approach show that it is fast and accurate. Moreover, it performs well on the ultrasound images, which has the common problem of the speckle noise

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Intima-Media Thickness: Setting a Standard for a Completely Automated Method of Ultrasound Measurement

The intima - media thickness (IMT) of the common carotid artery is a widely used clinical marker of severe cardiovascular diseases. IMT is usually manually measured on longitudinal B-Mode ultrasound images. Many computer-based techniques for IMT measurement have been proposed to overcome the limits of manual segmentation. Most of these, however, require a certain degree of user interaction. In this paper we describe a new completely automated layers extraction (CALEXia) technique for the segmentation and IMT measurement of carotid wall in ultrasound images. CALEXia is based on an integrated approach consisting of feature extraction, line fitting, and classification that enables the automated tracing of the carotid adventitial walls. IMT is then measured by relying on a fuzzy K-means classifier. We tested CALEXia on a database of 200 images. We compared CALEXia performances to those of a previously developed methodology that was based on signal analysis (CULEXsa). Three trained operators manually segmented the images and the average profiles were considered as the ground truth. The average error from CALEXia for lumen - intima (LI) and media - adventitia (MA) interface tracings were 1.46 ± 1.51 pixel (0.091 ± 0.093 mm) and 0.40 ± 0.87 pixel (0.025 ± 0.055 mm), respectively. The corresponding errors for CULEXsa were 0.55 ± 0.51 pixels (0.035 ± 0.032 mm) and 0.59 ± 0.46 pixels (0.037 ± 0.029 mm). The IMT measurement error was equal to 0.87 ± 0.56 pixel (0.054 ± 0.035 mm) for CALEXia and 0.12 ± 0.14 pixel (0.01 ± 0.01 mm) for CULEXsa. Thus, CALEXia showed limited performance in segmenting the LI interface, but outperformed CULEXsa in the MA interface and in the number of images correctly processed (10 for CALEXia and 16 for CULEXsa). Based on two complementary strategies, we anticipate fusing them for further IMT improvement

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Prostate Segmentation and Regions of Interest Detection in Transrectal Ultrasound Images

The early detection of prostate cancer plays a significant role in the success of treatment and outcome. To detect prostate cancer, imaging modalities such as TransRectal UltraSound (TRUS) and Magnetic Resonance Imaging (MRI) are relied on. MRI images are more comprehensible than TRUS images which are corrupted by noise such as speckles and shadowing. However, MRI screening is costly, often unavailable in many community hospitals, time consuming, and requires more patient preparation time. Therefore, TRUS is more popular for screening and biopsy guidance for prostate cancer. For these reasons, TRUS images are chosen in this research. Radiologists first segment the prostate image from ultrasound image and then identify the hypoechoic regions which are more likely to exhibit cancer and should be considered for biopsy. In this thesis, the focus is on prostate segmentation and on Regions of Interest (ROI)segmentation. First, the extraneous tissues surrounding the prostate gland are eliminated. Consequently, the process of detecting the cancerous regions is focused on the prostate gland only. Thus, the diagnosing process is significantly shortened. Also, segmentation techniques such as thresholding, region growing, classification, clustering, Markov random field models, artificial neural networks (ANNs), atlas-guided, and deformable models are investigated. In this dissertation, the deformable model technique is selected because it is capable of segmenting difficult images such as ultrasound images. Deformable models are classified as either parametric or geometric deformable models. For the prostate segmentation, one of the parametric deformable models, Gradient Vector Flow (GVF) deformable contour, is adopted because it is capable of segmenting the prostate gland, even if the initial contour is not close to the prostate boundary. The manual segmentation of ultrasound images not only consumes much time and effort, but also leads to operator-dependent results. Therefore, a fully automatic prostate segmentation algorithm is proposed based on knowledge-based rules. The new algorithm results are evaluated with respect to their manual outlining by using distance-based and area-based metrics. Also, the novel technique is compared with two well-known semi-automatic algorithms to illustrate its superiority. With hypothesis testing, the proposed algorithm is statistically superior to the other two algorithms. The newly developed algorithm is operator-independent and capable of accurately segmenting a prostate gland with any shape and orientation from the ultrasound image. The focus of the second part of the research is to locate the regions which are more prone to cancer. Although the parametric dynamic contour technique can readily segment a single region, it is not conducive for segmenting multiple regions, as required in the regions of interest (ROI) segmentation part. Since the number of regions is not known beforehand, the problem is stated as 3D one by using level set approach to handle the topology changes such as splitting and merging the contours. For the proposed ROI segmentation algorithm, one of the geometric deformable models, active contours without edges, is used. This technique is capable of segmenting the regions with either weak edges, or even, no edges at all. The results of the proposed ROI segmentation algorithm are compared with those of the two experts' manual marking. The results are also compared with the common regions manually marked by both experts and with the total regions marked by either expert. The proposed ROI segmentation algorithm is also evaluated by using region-based and pixel-based strategies. The evaluation results indicate that the proposed algorithm produces similar results to those of the experts' manual markings, but with the added advantages of being fast and reliable. This novel algorithm also detects some regions that have been missed by one expert but confirmed by the other. In conclusion, the two newly devised algorithms can assist experts in segmenting the prostate image and detecting the suspicious abnormal regions that should be considered for biopsy. This leads to the reduction the number of biopsies, early detection of the diseased regions, proper management, and possible reduction of death related to prostate cancer

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

Diagnosis of liver disease by computer- assisted imaging techniques: A literature review

Copyright © 2022 The authors. Diagnosis of liver disease using computer-aided detection (CAD) systems is one of the most efficient and cost-effective methods of medical image diagnosis. Accurate disease detection by using ultrasound images or other medical imaging modalities depends on the physician's or doctor's experience and skill. CAD systems have a critical role in helping experts make accurate and right-sized assessments. There are different types of CAD systems for diagnosing different diseases, and one of the applications is in liver disease diagnosis and detection by using intelligent algorithms to detect any abnormalities. Machine learning and deep learning algorithms and models play also a big role in this area. In this article, we tried to review the techniques which are utilized in different stages of CAD systems and pursue the methods used in preprocessing, extracting, and selecting features and classification. Also, different techniques are used to segment and analyze the liver ultrasound medical images, which is still a challenging approach to how to use these techniques and their technical and clinical effectiveness as a global approach