Brain Tumor Detection and Segmentation by Intensity Adjustment.
In recent years, Brain tumor detection and segmentation has created an interest on research areas. The process of identifying and segmenting brain tumor is a very tedious and time consuming task, since human physique has anatomical structure naturally. Magnetic Resonance Image (MRI) scan analysis is...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 8 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| 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=137490029&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137490029 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2019 vid: 43 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137490029 137490029 137490029 10.1007/s10916-019-1368-4 137490029 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Brain Tumor Detection and Segmentation by Intensity Adjustment. aug: au: Rajan, P. G. Sundar, C. affil: Christian College of Engineering and Technology, Oddanchatram, India sug: subj: Brain Neoplasms Diagnosis Brain Neoplasms Radiography Magnetic Resonance Imaging Methods Image Enhancement Methods Image Processing, Computer Assisted Human Female Male India Image Interpretation, Computer Assisted Computer Simulation Cluster Analysis Tomography, X-Ray Computed X-Rays Algorithms Quality Improvement Female Male ab: In recent years, Brain tumor detection and segmentation has created an interest on research areas. The process of identifying and segmenting brain tumor is a very tedious and time consuming task, since human physique has anatomical structure naturally. Magnetic Resonance Image (MRI) scan analysis is a powerful tool that makes effective detection of the abnormal tissues from the brain. Among different techniques, Magnetic Resonance Image (MRI) is a liable one which contains several modalities in scanning the images captured from interior structure of human brain. A novel hybrid energy-efficient method is proposed for automatic tumor detection and segmentation. The proposed system follows K-means clustering, integrated with Fuzzy C-Means (KMFCM) and active contour by level set for tumor segmentation. An effective segmentation, edge detection and intensity enhancement can detect brain tumor easily. For that, active contour with level set method has been utilized. The performance of the proposed approach has been evaluated in terms of white pixels, black pixels, tumor detected area, and the processing time. This technique can deal with a higher number of segmentation problem and minimum execution time by ensuring segmentation quality. Additionally, tumor area length in vertical and horizontal positions is determined to measure sensitivity, specificity, accuracy, and similarity index values. Further, tumor volume is computed. Knowledge of the information of tumor is helpful for the physicians for effective diagnosing in tumor for treatments. The entire experimentation was implemented in MATLAB environment and simulation results were compared with existing approaches. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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