Three-Phase Automatic Brain Tumor Diagnosis System Using Patches Based Updated Run Length Region Growing Technique.
Manually finding and segmenting brain tumor is a tedious process in MR brain images due to the unpredictable appearance of tissues with a different pattern, contour, mass, and positions. The proposed work has three phases automatic tumor diagnosis system for tumorous slice detection, segmentation, a...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 2; pp. 465 - 480 |
|---|---|
| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142764016&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142764016 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2020 vid: 33 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142764016 142764016 142764016 10.1007/s10278-019-00276-2 142764016 ppf: 465 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Three-Phase Automatic Brain Tumor Diagnosis System Using Patches Based Updated Run Length Region Growing Technique. aug: au: Kalaiselvi, T. Kumarashankar, P. Sriramakrishnan, P. affil: School of Computer Science and Technologies, Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), 624302, Gandhigram, Tamil Nadu, India sug: subj: Automation Magnetic Resonance Imaging Methods Brain Neoplasms Diagnosis Human Imaging, Three-Dimensional Methods Brain Neoplasms Pathology Sensitivity and Specificity Validity Predictive Value of Tests ab: Manually finding and segmenting brain tumor is a tedious process in MR brain images due to the unpredictable appearance of tissues with a different pattern, contour, mass, and positions. The proposed work has three phases automatic tumor diagnosis system for tumorous slice detection, segmentation, and visualization from MRI human head volumes. The proposed method has an automatic classification followed by segmentation and is called as patch-based updated run length region growing technique (PR2G). In the first phase, classification is done through training and testing process using SVM classifier with 8 × 8 patches. Three optimal features are chosen using infinite feature selection (IFS) method. The purpose of the first phase is to automatically cluster the input MRI image into a normal or tumorous slice and localize the tumor. The second phase aims to segment the tumor in abnormal tumorous slices identified by the first phase using run length region growing technique. Finally, the third phase contains a post metric evaluation like 3D tumor volume construction and estimation from actual and segmented tumor volume using Carelieri's estimator. Classification accuracy is measured using sensitivity, specificity, accuracy, and error rates also calculated using false alarm (FA) and missed alarm (MA). Segmentation accuracy is calculated using Dice similarity, positive predictive value (PPV), sensitivity, and accuracy. Datasets used for this experiment are collected from whole brain atlas (WBA) and BraTS repositories. Experimental results show that the PR2G achieves 97% of classification accuracy and 80% of Dice segmentation accuracy. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|