Development of an automated extraction method for liver tumors in three‐dimensional multiphase multislice images

Abstract

SUMMARYThis paper proposes an automatic method for extracting liver tumors from three-dimensional abdominal CT images of four phases (noncontrast, early phase, portal phase, late phase). In the proposed method, the liver region is extracted from the image of each phase. Enhancement process is applied to the region by using a three-dimensional adaptive convergence index filter. The local maximum points are extracted from the enhanced image and the candidate regions for the liver tumor are extracted by applying the region-growing process. Multiple features are derived from the extracted candidates and it is decided whether or not the candidate actually corresponds to a tumor, based on the Mahalanobis distance calculated from the features. The processing from the enhancement to the discrimination of the candidates is performed independently for each phase, and four processed images are generated. Finally, the resultant images are combined and output. The proposed method is applied to 40 three-dimensional CT images of 10 patients taken by a multislice CT scanner. The result is promising, since all 8 existing tumors were detected, and the number of false-positive regions per image was 0.

Area Extraction of the Liver and Hepatocellular Carcinoma in CT Scans

“…4 This approach, by using the feature that the central part of tumors appears more brightly than the backgrounds, selects local maxima of intensity values as candidate points and performs region growing processing, segmenting candidate regions of liver tumors. However, as shown in Figure 2, being quoted by Nakagawa et al, 4 because the hepatic portal vein or the vena cava has the similar distribution of intensity with liver tumors and the approach does not support the discrimination between the organs and liver tumors, regions of the organs may be wrongly segmented as candidate regions, thus influencing the region segmentation of liver tumors.…”

“…Thus, there is a need to develop a computer-aided diagnosis system. 4 This paper proposed a novel method that automatically provides information on position and morphology of hepatocellular carcinoma through the analysis of CT scans. The proposed method extracts an area of the liver and detects relative positions of hepatocellular carcinoma in the area by applying a sequence of digital image processing techniques to about 45-50 CT slices obtained by scanning by 2.5-mm intervals starting from the lower part of the chest.…”