Adrenal tumor characterization on magnetic resonance images. Issue 1 (25th July 2019)

• Record Type: Journal Article
• Authors: Barstugan, Mucahid; Ceylan, Rahime; Asoglu, Semih; Cebeci, Hakan; Koplay, Mustafa
• Is Part Of: International journal of imaging systems and technology. Volume 30:Issue 1(2020)
• Page Start: 252
• Page End: 265
• Publication Date: 2019-07-25
• Subjects: adrenal glands -- adrenal tumor classification -- feature extraction -- MR images -- segmentation; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22358
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Adrenal tumors occur on adrenal glands and are generally detected on abdominal area scans. Adrenal tumors, which are incidentally detected, release vital hormones. These types of tumors that can be malignant affect body metabolism. Both of benign and malign adrenal tumors can have a similar size, intensity, and shape, this situation may lead to wrong decision during diagnosis and characterization of tumors. Thus, biopsy is done to confirm diagnosis of tumor types. In this study, adrenal tumor characterization is handled by using magnetic resonance images. In this way, it is wanted that patient can be disentangled from one or more imaging modalities (some of them can includes X‐ray) and biopsy. An adrenal tumor image set, which includes five types of adrenal tumors and has 112 benign tumors and 10 malign tumors, was used in this study. Two data sets were created from the adrenal tumor image set by manually/semiautomatically segmented adrenal tumors and feature sets of these data sets are constituted by different methods. Two‐dimensional gray‐level co‐occurrence matrix (2D‐GLCM), gray‐level run‐length matrix (GLRLM), and two‐dimensional discrete wavelet transform (2D‐DWT) methods were analyzed to reveal the most effective features on adrenal tumor characterization. Feature sets were classified in two ways: benign/malign (binary classification) and type characterization (multiclass classification). Support vector machine and artificial neural network classified feature sets. The best performance on benign/malign classification was obtained by the 2D‐GLCM feature set. The best results were assessed with sensitivity, specificity, accuracy, precision, and F‐score metrics and they were 99.17%, 90%, 98.4%, 99.17%, and 99.13%, respectively. The highest classification performance on type characterization was obtained by the 2D‐DWT feature set as 59.62%, 96.17%, 93.19%, 54.69%, and 54.94% for sensitivity, specificity, accuracy, precision, and F‐score metrics, respectively.