Computer-aided diagnosis and decision-making system for medical data analysis: A case study on prostate MR images

Prostate cancer is the most common cancer in males and a major cause of cancer-related death.Magnetic resonance(MR)imaging is recently emerging as a powerful tool for prostate cancer diagnosis.To clinically diagnose prostate cancer,doctors need to segment the prostate area in the MR image.However,manual segmentation is time consuming and influenced by the physician’s experience.Computer-aided diagnosis and decision-making systems have shown great effectiveness in assisting doctors for this purpose.At the same time,deep learning based on Generative Adversarial Networks can be applied to the segmentation of prostate MR images.In this paper,we propose a new computer-aided diagnosis and decision-making system based on a deep learning model to automatically segment the prostate region from prostate MR images.Additionally,receptive field block(RFB)was integrated into the model to enhance the discriminability and robustness of the extracted multi-scale features.We also introduced dense upsampling convolution instead of the traditional bilinear interpolation to capture and recover fine-detailed information.Adversarial training was used to train the model,and the segmentation results were experimentally tested.The results showed that adversarial training and RFB are indeed effective,and the proposed method is superior to other methods on various evaluation metrics.

An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.

Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm

In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.

Edge Contour Extraction in MR Image Using Edgeflow Contour Model

This paper presents a new model for edge extraction of MR images, based on curve evolution and edgeflow techniques. At first the model for curve evolution is constructed, which automatically detect boundaries, and change of topology in terms of the edgeflow fields, and then the numerical approximation of the model is introduced, which is based on semi-implicit scheme to speed up the proposed approach. Finally, the numerical implementation is present and the experimental results show that the proposed model successfully extracts the edge contours, regardless of the heavy noise.

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes.Manual segmentation is crucial but time-consuming.Deep learning methods have emerged as key players in automating brain tumor segmentation.In this paper,we propose an efficient modified U-Net architecture,called EMU-Net,which is applied to the BraTS 2020 dataset.Our approach is organized into two distinct phases:classification and segmentation.In this study,our proposed approach encompasses the utilization of the gray-level co-occurrence matrix(GLCM)as the feature extraction algorithm,convolutional neural networks(CNNs)as the classification algorithm,and the chi-square method for feature selection.Through simulation results,the chi-square method for feature selection successfully identifies and selects four GLCM features.By utilizing the modified U-Net architecture,we achieve precise segmentation of tumor images into three distinct regions:the whole tumor(WT),tumor core(TC),and enhanced tumor(ET).The proposed method consists of two important elements:an encoder component responsible for down-sampling and a decoder component responsible for up-sampling.These components are based on a modified U-Net architecture and are connected by a bridge section.Our proposed CNN architecture achieves superior classification accuracy compared to existing methods,reaching up to 99.65%.Additionally,our suggested technique yields impressive Dice scores of 0.8927,0.9405,and 0.8487 for the tumor core,whole tumor,and enhanced tumor,respectively.Ultimately,the method presented demonstrates a higher level of trustworthiness and accuracy compared to existing methods.The promising accuracy of the EMU-Net study encourages further testing and evaluation in terms of extrapolation and generalization.

Hybrid Segmentation Approach for Different Medical Image Modalities

The segmentation process requires separating the image region into sub-regions of similar properties.Each sub-region has a group of pixels having the same characteristics,such as texture or intensity.This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)algorithms.An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques.The existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed algorithms.This paper extensively analyzes and summarizes the comparative investigation of these techniques.Finally,a prediction of the improvement involves the combination of these techniques is suggested.The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.