• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.171-176
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• 2005

This paper proposes an automatic liver segmentation method using improved partial histogram threshold (PHT) algorithms. This method removes neighboring abdominal organs regardless of random pixel variation of contrast enhanced CT images. Adaptive multi-modal threshold is first performed to extract a region of interest (ROI). A left PHT (LPHT) algorithm is processed to remove the pancreas, spleen, and left kidney. Then a right PHT (RPHT) algorithm is performed for eliminating the right kidney from the ROI. Finally, binary morphological filtering is processed for removing of unnecessary objects and smoothing of the ROI boundary. Ten CT slices of six patients (60 slices) were selected to evaluate the proposed method. As evaluation measures, an average normalized area and area error rate were used. From the experimental results, the proposed automatic liver segmentation method has strong similarity performance as the MSM by medical Doctor.

• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.189-194
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• 2004

Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of a pancreas in the abdomen. In this paper, an automatic liver segmentation method using a partial histogram threshold (PHT) algorithm is proposed for overcoming randomness of CE-CT images and removing the pancreas. After histogram transformation, adaptive multi-modal threshold is used to find the range of gray-level values of the liver structure. Also, the PHT algorithm is performed for removing the pancreas. Then, morphological filtering is processed for removing of unnecessary objects and smoothing of the boundary. Four CE-CT slices of eight patients were selected to evaluate the proposed method. As the average of normalized average area of the automatic segmented method II (ASM II) using the PHT and manual segmented method (MSM) are 0.1671 and 0.1711, these two method shows very small differences. Also, the average area error rate between the ASM II and MSM is 6.8339 ％. From the results of experiments, the proposed method has similar performance as the MSM by medical Doctor.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1349-1355
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• 1999

A power distribution facility map is drawn on cadastral map. Besides, grid lines are added on the map for sectionalization. For automatic recognition of the map, we first extract recognizing regions. In this paper, we propose an extraction method of recognizing regions by partially extending thinned image. The proposed method is consist of three phases, binarization phase, thinning phase and partial extending phase. The first phase generate a binary image using threshold value which is obtained by histogram analysis. The binary image contains many part of recognizing regions, but not all. The second phase generate thinned image which is generated by appling thinning operator to the binary image. And the third phase extends thinned image from terminal point until satisfying termination condition. The proposed method is tested on several power distribution facility maps, and the results are presented.

• Radiation Oncology Journal
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• v.25 no.4
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• pp.261-267
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• 2007

Purpose: To evaluate the association between radiation pneumonitis and dose-volume histogram parameters and to provide practical guidelines to prevent radiation pneumonitis following radiotherapy administered for breast cancer including internal mammary lymph nodes. Materials and Methods: Twenty patients with early breast cancer who underwent a partial mastectomy were involved in this study. The entire breast, supraclavicular lymph nodes, and internal mammary lymph nodes were irradiated with a dose of 50.4 Gy in 28 fractions. Radiation pneumonitis was assessed by both radiological pulmonary change (RPC) and by evaluation of symptomatic radiation pneumonitis. Dose-volume histogram parameters were compared between patients with grade <2 RPC and those with grade ${\geq}$2 RPC. The parameters were the mean lung dose, V10 (percent lung volume receiving equal to and more than 10 Gy), V20, V30, V40, and normal tissue complication probability (NTCP). Results: Of the 20 patients, 9 (45%) developed grade 2 RPC and 11 (55%) did not develop RPC (grade 0). Only one patient developed grade 1 symptomatic radiation pneumonitis. Univariate analysis showed that among the dose-volume histogram parameters, NTCP was significantly different between the two RPC grade groups (p<0.05). Fisher's exact test indicated that an NTCP value of 45% was appropriate as an RPC threshold level. Conclusion: This study shows that NTCP can be used as a predictor of RPC after radiotherapy of the internal mammary lymph nodes in breast cancer. Clinically, it indicates that an RPC is likely to develop when the NTCP is greater than 45%.