eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data

Gurugubelli, Venkata Sukumar; Li, Zhouzhou; Wang, Honggang; Fang, Hua

doi:10.1109/iccnc.2018.8390419

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…The nervous system is the most important organ involved in AIDS-related opportunistic infections, and 1/3 of AIDS patients are accompanied by neurological lesions [ 18 ]. Up to 80% of autopsy AIDS cases have found abnormalities in neuropathology.…”

Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Features of Acquired Immune Deficiency Syndrome Involving Central Nervous System Diseases by Intelligent Fuzzy C-Means Clustering (FCM) Algorithm

Huang

Chen

et al. 2022

Computational and Mathematical Methods in Medicine

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This study was aimed to explore the application of fuzzy C-means (FCM) algorithm in MR images of acquired immune deficiency syndrome (AIDS) patients. Sixty AIDS patients with central nervous disease were selected as the research object. A method of brain MR image segmentation based on FCM clustering optimization was proposed, and FCM was optimized based on the neighborhood pixel correlation of gray difference. The correlation was introduced into the objective function to obtain more accurate pixel membership and segmentation features of the image. The segmented image can retain the original image information. The proposed algorithm can clearly distinguish gray matter from white matter in images. The average time of image segmentation was 0.142 s, the longest time of level set algorithm was 2.887 s, and the running time of multithreshold algorithm was 1.708 s. FCM algorithm had the shortest running time, and the average time was significantly better than other algorithms ( P < 0.05 ). FCM image segmentation efficiency was above 90%, and patients can clearly display the location of lesions after MRI imaging examination. In summary, FCM algorithm can effectively combine the spatial neighborhood information of the brain image, segment the BRAIN MR image, analyze the characteristics of AIDS patients from different directions, and provide effective treatment for patients.

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Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Features of Acquired Immune Deficiency Syndrome Involving Central Nervous System Diseases by Intelligent Fuzzy C-Means Clustering (FCM) Algorithm

Huang

Chen

et al. 2022

Computational and Mathematical Methods in Medicine

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“…This is an essential function of an adaptive filter. The filter uses the window size as the input and manages the balance based on the input image [ 31 ]. The subsequent results were used to investigate noise elimination in brain tumor images using the adaptive filter shown in Figure 3 .…”

Section: Proposed Systemmentioning

confidence: 99%

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

et al. 2023

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To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.

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“…Fuzzy clustering is a kind of algorithm often applied in segmentation of the CS image, in which fuzzy c-means (FCM) algorithm is more widely adopted [4]. Improper selection of the initial clustering center will have a huge impact on the accumulation results.…”

Section: Introductionmentioning

confidence: 99%

Segmentation Algorithm for Intracranial Magnetic Resonance Images for Cerebral Stroke Identification and Nursing Evaluation

Shi

Kong

Tian

et al. 2021

Scientific Programming

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This study aimed to investigate the therapeutic effect and nursing evaluation of patients with cerebral stroke (CS) through intracranial magnetic resonance imaging (MRI) images under the condition of segmentation algorithm. 199 CS patients were selected and divided randomly into a control group (group A) and an experimental group (group B) based on different treatment methods. Patients of group A were given routine antithrombotic therapy, and patients of group B were treated with intravenous thrombolytic therapy under evaluation of segmentation algorithm-based MRI images. Then, there were comparisons on clinical therapeutic effect, neurological damage score, and daily living ability index score of all patients. After treatment, the total effective rate (92.12%) and daily life index (41.45 ± 11.24) of patients in group B were higher than those of group A ( P < 0.05). However, neurological damage scores (3.36 ± 1.13 points) of patients in group B after treatment were lower than those of group A (5.85 ± 2.31 points) ( P < 0.05). The routine clinical nursing satisfaction rate (79.8%) was lower than the overall satisfaction rate (97%) of the combination of clinical and imaging nursing ( P < 0.05). Therefore, there were greatly clinical therapeutic effects of intravenous thrombolysis evaluated by intracranial MRI images under segmentation algorithm for CS patients, and routine nursing could improve patients’ satisfaction, which were worthy of clinical promotion.

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eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data

Cited by 7 publications

References 22 publications

Magnetic Resonance Features of Acquired Immune Deficiency Syndrome Involving Central Nervous System Diseases by Intelligent Fuzzy C-Means Clustering (FCM) Algorithm

Magnetic Resonance Features of Acquired Immune Deficiency Syndrome Involving Central Nervous System Diseases by Intelligent Fuzzy C-Means Clustering (FCM) Algorithm

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

Segmentation Algorithm for Intracranial Magnetic Resonance Images for Cerebral Stroke Identification and Nursing Evaluation

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