Adrenal tumor segmentation method for MR images. (October 2018)

• Record Type: Journal Article
• Authors: Barstuğan, Mücahid; Ceylan, Rahime; Asoglu, Semih; Cebeci, Hakan; Koplay, Mustafa
• Is Part Of: Computer methods and programs in biomedicine. Volume 164(2018)
• Page Start: 87
• Page End: 100
• Publication Date: 2018-10
• Subjects: Adrenal tumor segmentation -- CAD system -- Hybrid approach -- MR images; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.07.009
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Adrenal tumors can be adherent to liver, spleen, spinal cord, kidney, and this situation prevents an accurate segmentation of adrenal tumors. In addition, low contrast, non-standardized shape and size, homogeneity, and heterogeneity of the tumors are considered as problems in segmentation. This study proposes a computer-aided diagnosis (CAD) system to segment adrenal tumors by eliminating the above problems. The performance of the proposed method was assessed on 113 Magnetic Resonance (MR) images using seven statistical metrics. As a result, an efficient framework is presented on segmentation of adrenal tumors for MR images, especially for cyst-based tumors. Abstract: Background and objective: Adrenal tumors, which occur on adrenal glands, are incidentally determined. The liver, spleen, spinal cord, and kidney surround the adrenal glands. Therefore, tumors on the adrenal glands can be adherent to other organs. This is a problem in adrenal tumor segmentation. In addition, low contrast, non-standardized shape and size, homogeneity, and heterogeneity of the tumors are considered as problems in segmentation. Methods: This study proposes a computer-aided diagnosis (CAD) system to segment adrenal tumors by eliminating the above problems. The proposed hybrid method incorporates many image processing methods, which include active contour, adaptive thresholding, contrast limited adaptive histogram equalization (CLAHE), image erosion, and region growing. Results: The performance of the proposed method was assessed on 113 Magnetic Resonance (MR) images using seven metrics: sensitivity, specificity, accuracy, precision, Dice Coefficient, Jaccard Rate, and structural similarity index (SSIM). The proposed method eliminates some of the discussed problems with success rates of 74.84%, 99.99%, 99.84%, 93.49%, 82.09%, 71.24%, 99.48% for the metrics, respectively. Conclusions: This study presents a new method for adrenal tumor segmentation, and avoids some of the problems preventing accurate segmentation, especially for cyst-based tumors.