改进的数学形态学开闭滤波算法在细胞图像预处理中的应用

对数学形态学开闭滤波算法进行了改进,使其能针对癌细胞图像特征进行开闭滤波,在滤除背景噪声的同时,能滤除正常的离散细胞,保留疑似癌变细胞的全部原始图像特征.与其他几种滤波方法在癌细胞图像中应用的结果相比较,改进后的数学形态学开闭滤波算法能够很好地保留原始灰度信息,并且能最大限度地去除背景噪声及无用信息.

基于数学形态学的医学图像自动阈值分割

提出了一种自动分割癌细胞图像的方法.该方法利用数学形态学开运算型边缘检测算子对图像边缘锐化;利用邻域平均与中值滤波方法平滑图像;利用改进的判别分析法自动选取灰度阈值进行图像自动分割;利用自动分割区域与手动分割区域的误差和平均最短距离对自动分割效果进行评价.用该方法对20幅癌细胞图像进行自动分割实验,证明了其对图像分割的有效性.

Primary clear cell carcinoma in the liver: CT and MRI findings

AIM: To retrospectively analyze the computed tomography (CT) and magnetic resonance imaging (MRI) appearances of primary clear cell carcinoma of the liver (PCCCL) and compare the imaging appearances of PCCCL and common type hepatocellular carcinoma (CHCC) to determine whether any differences exist between the two groups. METHODS: Twenty cases with pathologically proven PCCCL and 127 cases with CHCC in the Second Affiliated Hospital of Sun Yat-sen University were included in this study. CT or MRI images from these patients were retrospectively analyzed. The following imaging findings were reviewed: the presence of liver cirrhosis, tumor size, the enhancement pattern on dynamic contrast scanning, the presence of pseudo capsules, tumor rupture, portal vein thrombosis and lymph node metastasis. RESULTS: Both PCCCL and CHCC were prone to occur in patients with liver cirrhosis, the association rate of liver cirrhosis was 80.0% and 78.7%, respectively (P > 0.05). The mean sizes of PCCCL and CHCC tumors were (7.28 ± 4.25) cm and (6.96 ± 3.98) cm, respectively. Small HCCs were found in 25.0% (5/20) of PCCCL and 19.7% (25/127) of CHCC cases. No significant differences in mean size and ratio of small HCCs were found between the two groups (P = 0.658 and 0.803, respectively). Compared with CHCC patients, PCCCL patients were more prone to form pseudo capsules (49.6% vs 75.0%, P = 0.034). Tumor rupture, typical HCC enhancement patterns and portal vein tumor thrombosis were detected in 15.0% (3/20), 72.2% (13/18) and 20.0% (4/20) of patients with PCCCL and 3.1% (4/127), 83.6% (97/116) and 17.3% (22/127) of patients with CHCC, respectively. There were no significant differences between the two groups (all P > 0.05). No patients with PCCCL and 2.4% (3/127) of patients with CHCC showed signs of lymph node metastasis (P > 0.05). CONCLUSION: The imaging characteristics of PCCCL are similar to those of CHCC and could be useful for differentiating these from other liver tumors (such as hemangioma and hepatic metastases). PCCCLs are more prone than CHCCs to form pseudo capsules.

Study of the impact of CT/CT image fusion radiotherapy on V_(20) and radiation pneumonitis of non-small cell lung cancer

Objective:The aim of our study was to investigate the value of CT/CT image fusion radiation treatment planning in non-small cell lung cancer(NSCLC) and the impact on V20 and radiation pneumonitis(RP).Methods:Patients who were pathologically or cytologically diagnosed of stage IIIA and IIIB NSCLC were treated with three-dimensional conformal radiation therapy(4000 cGy).Forty patients got at least 25% tumor reduction were randomly divided into two groups:group A of regular shrink field radiotherapy(20 cases) and group B of CT/CT image fused shrink field radiotherapy(20 cases).Dosage reached 6600 cGy.Clinical data,V20 and RP were observed within 3 months after radiotherapy.Statistical analysis was conducted for the NSCLC patients.Results:22.5%(9/40) patients got RP during follow-up.Group A accounted for 6 cases(30%),and group B had 3 cases(15%).There was no marked difference between the two groups(P = 0.256),univariate analysis revealed that the IV20 of A and B groups,and IV20 and CV20 of all patients were statistically related to the incidence of RP(P < 0.05).With Wilcoxon method assay,the ipsilateral lung V20 and contralateral lung V20 had statistical significance between the two groups(P < 0.05).Conclusion:The CT/CT image infusion treatment planning could increase the radical dosage with better tumor control probability but won't increase adverse reaction.

Automatic segmentation of Colon Cancer Cells Based on Active Contour Method： A New Approach

Automatic interpretation of the images of colon cell biopsies requires automatic segmentation of these cells in the image obtained. The active contour method for image segmentation is a well known method for automatic detection of the cell contour. However, the application of this method on colon cell images was not effective. In this paper, the authors have proposed a new technique to reduce the analysis time needed to detect cells in a given image. This technique is based on the active contour method but now using a progressive division of the dimensions of the image to achieve convergence. The model proposed succeeded in detecting cells whose boundaries are not necessarily defined by a gradient. The initial curve can be anywhere in the image, and interior contours can be automatically detected. The developed algorithm was successfully applied on textured multispectral images of three types of cells, including benign hyperplasia （BH）, intraepithelial neoplasia （IN）, and carcinoma （Ca） cells.

Pathological clavicular fracture as first presentation of renal cell carcinoma: a case report and literature review

Renal cell carcinoma(RCC) accounts for approximately 3% of all cancer cases. RCCs usually metastasize to the lungs, bones, liver, or brain. Only <1% of patients with bone metastases manifested clavicular RCC metastases. Thus, clavicular metastasis as the initial presentation of RCC is extremely rare. We report a patient with RCC metastasis to the left clavicle, which was first presented with pain caused by a pathological fracture. Magnetic resonance image revealed a renal tumor, and technetium-99m–methylene diphosphonate bone scintigraphy showed multiple osseous metastases. The patient eventually underwent surgery to remove the lateral end of the left clavicle and right kidney. Histopathology revealed renal tumor and clear cell carcinoma in the clavicle. Finally, we review 17 cases of clavicular metastases originating from different malignancies.

Automated Classification of Segmented Cancerous Cells in Multispectral Images

Automatic reading procedures in colon cells biopsies allow a faster and precise reading of microscopic biopsies. These procedures implement automatic image segmentation in order to classify cell types as cancerous or noncancerous. The authors have developed a new approach aiming to detect colon cancer cells derived from the ＂Snake＂ method but using a progressive division of the dimensions of the image to achieve rapid segmentation. The aim of the present paper was to classify different cancerous cell types based on nine morphological parameters and on probabilistic neural network. Three types of cells were used to assess the efficiency of our classifications models, including BH （Benign Hyperplasia）, IN （Intraepithelial Neoplasia） that is a precursor state for cancer, and Ca （Carcinoma） that corresponds to abnormal tissue proliferation （cancer）. Results showed that among the nine parameters used to classify cells, only three morphologic parameters （area, Xor convex and solidity） were found to be effective in distinguishing the three types of cells. In addition, classification of unknown cells was possible using this method.