{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:46:21Z","timestamp":1760402781855,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T00:00:00Z","timestamp":1586908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Background: The purpose of this article is to provide a new evaluation tool based on skeleton maps to assess the tumoral and non-tumoral regions of the 2D MRI in PD-weighted (proton density) and T2w (T2-weighted type) brain images. Methods: The proposed method investigated inter-hemisphere brain tissue similarity using a mask in the right hemisphere and its mirror reflection in the left one. At the hemisphere level and for each ROI (region of interest), a morphological skeleton algorithm was used to efficiently investigate the similarity between hemispheres. Two datasets with 88 T2w and PD images belonging to healthy patients and patients diagnosed with glioma were investigated: D1 contains the original raw images affected by Rician noise and D2 consists of the same images pre-processed for noise removal. Results: The investigation was based on structural similarity assessment by using the Structural Similarity Index (SSIM) and a modified Jaccard metrics. A novel S-Jaccard (Skeleton Jaccard) metric was proposed. Cluster accuracy was estimated based on the Silhouette method (SV). The Silhouette coefficient (SC) indicates the quality of the clustering process for the SSIM and S-Jaccard. To assess the overall classification accuracy an ROC curve implementation was carried out. Conclusions: Consistent results were obtained for healthy patients and for PD images of glioma. We demonstrated that the S-Jaccard metric based on skeletal similarity is an efficient tool for an inter-hemisphere brain similarity evaluation. The accuracy of the proposed skeletonization method was smaller for the original images affected by Rician noise (AUC = 0.883 (T2w) and 0.904 (PD)) but increased for denoised images (AUC = 0.951 (T2w) and 0.969 (PD)).<\/jats:p>","DOI":"10.3390\/computation8020031","type":"journal-article","created":{"date-parts":[[2020,4,15]],"date-time":"2020-04-15T09:19:50Z","timestamp":1586942390000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Brain Tissue Evaluation Based on Skeleton Shape and Similarity Analysis between Hemispheres"],"prefix":"10.3390","volume":"8","author":[{"given":"Lenuta","family":"Pana","sequence":"first","affiliation":[{"name":"Faculty of Sciences and Environment, Department of Chemistry, Physics&amp; Environment, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania"},{"name":"The Modelling &amp; Simulation Laboratory, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5934-329X","authenticated-orcid":false,"given":"Simona","family":"Moldovanu","sequence":"additional","affiliation":[{"name":"The Modelling &amp; Simulation Laboratory, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania"},{"name":"Faculty of Automation, Computers, Electrical Engineering and Electronics, Department of Computer Science and Information Technology, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8437-498X","authenticated-orcid":false,"given":"Nilanjan","family":"Dey","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Techno India College of Technology, West Bengal 700156, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3217-6185","authenticated-orcid":false,"given":"Amira S.","family":"Ashour","sequence":"additional","affiliation":[{"name":"Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta 31527, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9121-5714","authenticated-orcid":false,"given":"Luminita","family":"Moraru","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Environment, Department of Chemistry, Physics&amp; Environment, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania"},{"name":"The Modelling &amp; Simulation Laboratory, Dunarea de Jos University of Galati, Galati 47 Domneasca Str., 800008 Galati, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,15]]},"reference":[{"key":"ref_1","first-page":"3942","article-title":"Segmentation of Brain Tumor Using K-Means Clustering Algorithm","volume":"13","author":"Kumar","year":"2018","journal-title":"J. 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