Computer‐aided detection of renal calculi from noncontrast CT images using TV‐flow and MSER features. Issue 1 (22nd December 2014)

• Record Type: Journal Article
• Authors: Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M.
• Is Part Of: Medical physics. Volume 42:Issue 1(2015)
• Page Start: 144
• Page End: 153
• Publication Date: 2014-12-22
• Subjects: computerised tomography -- diseases -- feature extraction -- image classification -- image denoising -- kidney -- medical image processing -- support vector machines; Computed tomography -- Contrast -- Smoothing -- Computer‐aided diagnosis -- Endocrine diseases -- Noise; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image -- Inference methods or devices; renal calculi -- kidney stone -- total variation flow -- maximally stable extremal regions -- computer‐aided detection; Kidneys -- Computed tomography -- Calculus -- Medical image smoothing -- Diffusion -- Medical image noise -- Medical image segmentation -- Anisotropy -- Image detection systems; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4903056
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer‐aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% ( p < 1 e − 3) on all calculi from 1 to 433 mm 3 in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV‐flow and MSER features are efficient means to robustly and accurately detect renal calculi on low‐dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.