CA : A Cancer Journal for Clinicians

Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.

Automated medical image segmentation techniques

Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures.

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Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks

18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is now routinely used in oncological imaging for diagnosis and staging and increasingly to determine early response to treatment, often employing semiquantitative measures of lesion activity such as the standardized uptake value (SUV). However, the ability to predict the behaviour of a tumour in terms of future therapy response or prognosis using SUVs from a baseline scan prior to treatment is limited. It is recognized that medical images contain more useful information than may be perceived with the naked eye, leading to the field of “radiomics” whereby additional features can be extracted by computational postprocessing techniques. In recent years, evidence has slowly accumulated showing that parameters obtained by texture analysis of radiological images, reflecting the underlying spatial variation and heterogeneity of voxel intensities within a tumour, may yield additional predictive and prognostic information. It is hoped that measurement of these textural features may allow better tissue characterization as well as better stratification of treatment in clinical trials, or individualization of future cancer treatment in the clinic, than is possible with current imaging biomarkers. In this review we focus on the literature describing the emerging methods of texture analysis in 18FDG PET/CT, as well as other imaging modalities, and how the measurement of spatial variation of voxel grey-scale intensity within an image may provide additional predictive and prognostic information, and postulate the underlying biological mechanisms.

Quantifying Tumour Heterogeneity in 18F-FDG PET/CT Imaging by Texture Analysis

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

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Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

The objective of developing this software is to achieve auto-segmentation and tissue characterization. Therefore, the present algorithm has been designed and developed for analysis of medical images based on hybridization of syntactic and statistical approaches, using artificial neural network (ANN). This algorithm performs segmentation and classification as is done in human vision system, which recognizes objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. The analysis of medical image is directly based on four steps: 1) image filtering, 2) segmentation, 3) feature extraction, and 4) analysis of extracted features by pattern recognition system or classifier. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features with ANN as segmentation and classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semisupervised approach in which supervision is involved only at the level of defining texture-primitive cell; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. The algorithm was first tested on Markov textures, and the success rate achieved in classification was 100%; further, the algorithm was able to give results on the test images impregnated with distorted Markov texture cell. In addition to this, the output also indicated the level of distortion in distorted Markov texture cell as compared to standard Markov texture cell. Finally, algorithm was applied to selected medical images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated.

Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network

Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.

This paper presents the automated detection and classification of histopathological images of cancer cells using morphological features. The manual assessment of disease is time-consuming and varies with the perception and the level of expertise of the pathologists. The judgment is based on the tissue structures, distribution of cells in tissue and the irregularities of cell shape and size. To overcome the limitation of manual diagnosis, a computer aided diagnosis based on the morphological features has been implemented for accurate and reliable detection of cancer. A dataset of 70 histopathological images of non-cancerous and cancerous tissues are randomly selected. The proposed work aims at developing the technique that uses reliable quantitative measures for providing objective and reproducible information complementary to that of a pathologist.

Classification of Histopathological images of Breast Cancerous and Non Cancerous Cells Based on Morphological features

The aim of this paper is to discuss important issues for design and development of noninvasive blood glucometer. Noninvasive blood glucose monitoring could improve life quality of diabetes patients, enabling a better regulation of hyper and hypoglycemia conditions. Research to develop noninvasive glucose sensing technology is driven by the promise to provide in vivo glucose concentrations without pain, cost, and inconvenience of test strip meters. The optical approaches have brought exciting advances to this field. Till date several methods have been proposed for non invasive measurement of glucose level, present paper discusses their principle advantages and limitations. Most of these criteria center on selectivity of the analytical measurement, signal to noise ratio (SNR) of the instrumentation, physical and chemical properties of the measurement site, and robustness of the calibration model.

Challenges & Countermeasures in Optical Noninvasive Blood Glucose Detection

To find accurate & reliable result in image analysis, it is important that image is processed and analyzed using image processing suitable AI technique further at the same time it is highly desired that processing time must be minimum. Preprocessing of the image makes it more clear and visible, while parallelizing of the algorithm optimizes the speed at which the image is processed. This paper explores current multi-core architectures available in commercial processors in order to speed up the image processing tasks. Parallel Implementation of Many sequential algorithms of Image processing was examined and analyzed in test and achieved good result if all the recourses are efficiently used. Main objective of this paper is to design some parallel image processing algorithms like segmentation, noise reduction, features calculation, histogram equalization etc by using Multi Core architecture and comparative study with some sequential image processing algorithm. These parallel algorithms are able to work with different number of thread, so as to take all the benefits of the upcoming processors having any number of cores. As medical imaging refers to view the human body in order to diagnose, monitor and treatment planning. This paper also describes the application of parallel computing applied in different Medical Imaging techniques like CT, PET scans etc.

Shiru Sharma

Papers

Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network

Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.

Classification of Histopathological images of Breast Cancerous and Non Cancerous Cells Based on Morphological features

Challenges & Countermeasures in Optical Noninvasive Blood Glucose Detection

Image Processing Tasks using Parallel Computing in Multi core Architecture and its Applications in Medical Imaging