基于小波变换的新疆地方性肝包虫CT 图像分类研究

采用基于 sym4和 db4小波基两种小波变换方法,探讨对新疆地方性肝包虫 CT 图像的分类价值。使用 sym4和 db4小波两种小波基,提取感兴趣病灶区的纹理特征,并通过统计学方法筛选出特征子集,采用C4．5决策树算法构建正常肝脏和多子囊型病变肝脏 CT 图像的计算机分类模型,并对模型进行准确性、灵敏度和特异性的验证评估。结果显示,对正常肝脏和多子囊型肝包虫进行分类,sym4小波的识别正确率为92．5％,db4小波的识别正确率为97．5％。实验结果表明,小波变换法所提取的纹理特征对识别正常肝脏和多子囊型肝包虫 CT 影像有较好的意义,也为后续的基于内容的新疆地方性肝包虫病的诊断系统奠定了基础。     

Characterization of Primary and Secondary Malignant Liver Lesions from B-Mode Ultrasound

Characterization of hepatocellular carcinomas (HCCs) and metastatic carcinomas (METs) from B-mode ultrasound presents a daunting challenge for radiologists due to their highly overlapping appearances. The differential diagnosis between HCCs and METs is often carried out by observing the texture of regions inside the lesion and the texture of background liver on which the lesion has evolved. The present study investigates the contribution made by texture patterns of regions inside and outside of the lesions for binary classification between HCC and MET lesions. The study is performed on 51 real ultrasound liver images with 54 malignant lesions, i.e., 27 images with 27 solitary HCCs (13 small HCCs and 14 large HCCs) and 24 images with 27 MET lesions (12 typical cases and 15 atypical cases). A total of 120 within-lesion regions of interest and 54 surrounding lesion regions of interest are cropped from 54 lesions. Subsequently, 112 texture features (56 texture features and 56 texture ratio features) are computed by statistical, spectral, and spatial filtering based texture features extraction methods. A two-step methodology is used for feature set optimization, i.e., feature pruning by removal of nondiscriminatory features followed by feature selection by genetic algorithm–support vector machine (SVM) approach. The SVM classifier is designed based on optimum features. The proposed computer-aided diagnostic system achieved the overall classification accuracy of 91.6 % with sensitivity of 90 % and 93.3 % for HCCs and METs, respectively. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in diagnosing liver malignancies.     

基于纹理特征的神经网络分类器用于肝硬化核磁共振图像分类识别

目的 研究基于纹理特征的神经网络分类器用于肝硬化核磁共振图像(MRI)分类诊断方法.方法 选取经大连医科大学附属第二医院临床和实验室检查确诊的18例患者的肝脏MR图像,其中肝硬化10例,正常肝脏8例,通过手工分割共获取MR图像感兴趣区(ROI)170个(肝硬化组88个,正常肝脏组82个).通过灰度共生矩阵提取了2组170个ROI0°、45°、90°、135°4个方向的纹理特征参数(共计56个),采用盒状图评估56个纹理特征参数区分肝硬化和正常肝脏的性能,获得2组间可分性好的纹理特征参数24个.分别采用全部的56个纹理特征参数(特征组A)、完全随机选择24个纹理特征参数(特征组B)及两组间可分性好的24个纹理特征参数(特征组C)训练反向传播(BP)神经网络,其中用于网络训练的ROI为110个,而测试BP神经网络的ROI为60个.结果 盒状图评价显示0°,45°,90°,135°4个方向上的能量、对比度、相关性、逆差矩、和方差以及差平均共计24个特征参数在肝硬化组和正常肝脏组间可分性较好.特征组C的正确识别率最高(95.00%,57/60),高于特征组A和特征组B(78.33%,47/60;88.33%,53/60;P＜0.05).结论 基于纹理特征的BP神经网络分类器适于肝硬化和正常肝脏MR图像的分类识别.     

Thyroid Nodule Recognition Based on Feature Selection and Pixel Classification Methods

Statistical approach is a valuable way to describe texture primitives. The aim of this study is to design and implement a classifier framework to automatically identify the thyroid nodules from ultrasound images. Using rigorous mathematical foundations, this article focuses on developing a discriminative texture analysis method based on texture variations corresponding to four biological areas (normal thyroid, thyroid nodule, subcutaneous tissues, and trachea). Our research follows three steps: automatic extraction of the most discriminative first-order statistical texture features, building a classifier that automatically optimizes and selects the valuable features, and correlating significant texture parameters with the four biological areas of interest based on pixel classification and location characteristics. Twenty ultrasound images of normal thyroid and 20 that present thyroid nodules were used. The analysis involves both the whole thyroid ultrasound images and the region of interests (ROIs). The proposed system and the classification results are validated using the receiver operating characteristics which give a better overall view of the classification performance of methods. It is found that the proposed approach is capable of identifying thyroid nodules with a correct classification rate of 83 % when whole image is analyzed and with a percent of 91 % when the ROIs are analyzed.     

Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images

The purpose of this study was to apply a novel method of multiscale echo texture analysis for distinguishing benign (hemangiomas) from malignant (hepatocellular carcinomas (HCCs) and metastases) focal liver lesions in B-mode ultrasound images. In this method, regions of interest (ROIs) extracted from within the lesions were decomposed into subimages by wavelet packets. Multiscale texture features that quantify homogeneity of the echogenicity were calculated from these subimages and were combined by an artificial neural network (ANN). A subset of the multiscale features was selected that yielded the highest performance in the classification of lesions measured by the area under the receiver operating characteristic curve (Az). In an analysis of 193 ROIs consisting of 50 hemangiomas, 87 hepatocellular carcinomas and 56 metastases, the multiscale features yielded a high A: value of 0.92 in distinguishing benign from malignant lesions, 0.93 in distinguishing hemangiomas from HCCs and 0.94 in distinguishing hemangiomas from metastases. Our new multiscale texture analysis method can effectively differentiate malignant from benign lesions, and thus has the potential to increase the accuracy of diagnosis of focal liver lesions in ultrasound images.     

Hepatocellular carcinoma in chronic hepatitis C: from bench to bedside

Hepatocellular carcinoma (HCC) is the fifth most common cancer in men and seventh in women, accounting for 7 % of all cancers, and the third cause of cancer-related death worldwide. Nearly 90 to 95 % of all HCC occur in the context of known and often preventable risk factors, such as chronic viral hepatitis, alcohol abuse, and metabolic disorders. Although several experimental lines of research support a direct role for hepatitis C virus (HCV) in cancer promotion, cirrhosis is the main risk factor for this tumor, whereas other factors like alcohol and tobacco smoking are clearly able to accelerate HCC development. For this reason, cirrhotic patients with chronic HCV infection are subjected to abdominal ultrasound surveillance every 6 months, aimed at an early diagnosis of HCC to allow curative treatment options. Current strategies to positively impact on HCC incidence rates in HCV patients include prevention of cirrhosis development by avoiding metabolic, pharmacological, or social factors associated with accelerated progression of liver disease, or through virus eradication by interferon-based treatments. Moreover, a successful antiviral treatment has the added benefit of positively impacting on the rate of HCC development also in patients who are already cirrhotic.     

Wavelet based R-peak detection for heart rate variability studies

Detection of QRS complex in electrocardiogram (ECG) signals is of immense importance in cardiac health prognosis. In this paper a new symmetric wavelet for detection of R-peak is presented, which has been designed based on spectral characteristics and morphology of QRS complex. The detection of R-peak was carried out using this designed wavelet, and with existing symmetric wavelets such as db3, db6, haar and bior2.2. The detection accuracy with this wavelet is 99.99%, which is higher than those with existing symmetric wavelets. The algorithm has been tested on standard databases such as Fantasia database of normal and healthy subjects, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia database, and on self-recorded electrocardiograms of normal subjects and patients under diseased stress. The study of heart rate variability (HRV) through computation of RR-tachogram using the new wavelet has proved to be effective in reliably evaluating HRV parameters.     

A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound

Abstract

A comparative study of three computer-aided classification (CAC) systems for characterization of focal hepatic lesions (FHLs), such as cyst, hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET), along with normal (NOR) liver tissue is carried out in the present work. In order to develop efficient CAC systems a comprehensive and representative dataset consisting of B-mode ultrasound images with (1) typical and atypical cases of cyst, HEM and MET lesions, (2) small and large HCC lesions and (3) NOR liver cases have been used for designing K-nearest neighbour (KNN), probabilistic neural network (PNN) and a back propagation neural network (BPNN) classifiers. For differential diagnosis between atypical FHLs, expert radiologists often visualize the textural characteristics of regions inside and outside the lesion. Accordingly in the present work, texture features and texture ratio features are computed from regions inside and outside the lesions. A feature set consisting of 208 texture features (i.e. 104 texture features and 104 texture ratio features) is subjected to principal component analysis (PCA) for dimensionality reduction; it is observed that maximum accuracy of 87.7% is obtained for a PCA-BPNN-based CAC system in comparison to 86.1% and 85% as obtained by PCA-PNN and PCA-KNN-based CAC systems. The sensitivity of the proposed PCA-BPNN based CAC system for NOR, Cyst, HEM, HCC and MET cases is 82.5%, 96%, 93.3%, 90% and 82.2%, respectively. The sensitivity values with respect to typical, atypical, small HCC and large HCC cases are 85.9%, 88.1%, 100% and 87%, respectively. Keeping in view the comprehensive and representative dataset used for designing the classifier, the results obtained by the proposed PCA-BPNN-based CAC system are quite encouraging and indicate its usefulness to assist experienced radiologists for interpretation and diagnosis of FHLs.     

Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation

Architecture distortion (AD) is an important and early sign of breast cancer, but due to its subtlety, it is often missed on the screening mammograms. The objective of this study is to create a quantitative approach for texture classification of AD based on various texture models, using support vector machine (SVM) classifier. The texture analysis has been done on the region of interest (ROI) selected from the original mammogram. A comprehensive analysis has been done on samples from three databases; out of which, two data sets are from the public domain, and the third data set is for clinical evaluation. The public domain databases are IRMA version of digital database for screening mammogram (DDSM) and Mammographic Image Analysis Society (MIAS). For clinical evaluation, the actual patient’s database has been obtained from ACE Healthways, Diagnostic Centre Ludhiana, India. The significant finding of proposed study lies in appropriate selection of the size of ROIs. The experiments have been done on fixed size of ROIs as well as on the ground truth (variable size) ROIs. Best results pertain to an accuracy of 92.94 % obtained in case of DDSM database for fixed-size ROIs. In case of MIAS database, an accuracy of 95.34 % is achieved in AD versus non-AD (normal) cases for ground truth ROIs. Clinically, an accuracy of 88 % was achieved for ACE dataset. The results obtained in the present study are encouraging, as optimal result has been achieved for the proposed study in comparison with other related work in the same area.     

Difference in the three-dimensional structure of the sinusoids between hepatocellular carcinoma and cirrhotic liver

The purpose of the present study was to estimate the difference in three-dimensional (3-D) structure of sinusoids between hepatocellular carcinoma (HCC) and cirrhotic liver, by the use of topology. Ten surgically resected lesions of HCC and 10 lesions of liver cirrhosis (LC) were used. Computer-alded reconstruction models of HCC sinusoids and LC sinusoids were developed from 20 4μm thick serial tissue sections from each specimen. A topologlcal invariant, called the first Bettl numberp., was used to estimate the complexity degree of the 3-D sinusoidal structure. The mean p, of the sinusoidal network in the examined tissue, 200X200X80 μm³ in size, was 46.5X33.0 In 10 HCC and 84.9 ±19.1 in 10 cirrhotic livers. There was a statistically significant difference between the two values (P<0.01), while there was no significant difference in the sinusoidal volume of the same size tissue between the HCC and the cirrhotic liver. It was found, therefore, that the sinusoidal network of HCC was more sparsely and coarsely knit in 3-D space than that of the cirrhotic liver.     

A wavelet-based algorithm for detecting clustered microcalcifications in digital mammograms.

A computerized scheme to detect clustered microcalcifications in digital mammograms has been developed. Detection of individual microcalcifications in regions of interest (ROIs) was also performed. The mammograms were previously classified into fatty and dense, according to their breast tissue. The most appropriate wavelet basis and reconstruction levels were selected. To select the wavelet basis, 40 profiles of microcalcifications were decomposed and reconstructed using different types of wavelet functions and different combinations of wavelet coefficients. The symlets with a basis of length 8 were chosen for fatty tissue. For dense tissue, the Daubechies' wavelets with a four-element basis were employed. Two methods to detect individual microcalcifications were evaluated: (a) two-dimensional wavelet transform, and (b) one-dimensional wavelet transform. The second technique yielded the best results, and was used to detect clustered microcalcifications in the complete mammogram. When detecting individual microcalcifications by using two-dimensional wavelet transform we have obtained, for fatty ROIs, a sensitivity of 71.11% at a false positive rate of 7.13 per image. For dense ROIs the sensitivity was 60.76% and the false positive rate, 7.33. The areas (A1) under the AFROC curves were 0.33+/-0.04 and 0.28+/-0.02, respectively. The one-dimensional wavelet transform method yielded 80.44% of sensitivity and 6.43 false positives per image (A1=0.39+/-0.03) for fatty ROIs, and 62.17% and 5.82 false positives per image (A1=0.37+/-0.02) for dense ROIs. For the detection of clusters of microcalcifications in the entire mammogram, the sensitivity was 80.00% with 0.94 false positives per image (A1=0.77+/-0.09) for fatty mammograms, and 72.85% of sensitivity at a false positive detection rate of 2.21 per image (A1=0.64+/-0.07) for dense mammograms. Globally, a sensitivity of 76.43% at a false positive detection rate of 1.57 per image was obtained.     

SVM-Based CAC System for B-Mode Kidney Ultrasound Images

The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee’s sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.     

Pathology of hepatocellular carcinoma and its precursors using proton magnetic resonance spectroscopy and a statistical classification strategy

AIM: Apply the statistical classification strategy (SCS) to magnetic resonance spectroscopy (MRS) data from liver biopsies and test its potential to discriminate between normal liver, cirrhotic nodules and nodules of hepatocellular carcinoma with a high degree of accuracy. METHODS: Liver tissue specimens from 54 patients undergoing either partial (hemi) or total hepatectomy were analysed by one-dimensional proton MRS at 8.5 Tesla. Histologically, these specimens were confirmed as normal (n=31), cirrhotic (n=59), and hepatocellular carcinoma (HCC, n=32). Diagnostic correlation was performed between the MR spectra and histopathology. An SCS was applied consisting of pre-processing MR magnitude spectra to identify spectral regions of maximal discriminatory value, and cross-validated linear discriminant analysis. RESULTS: SCS applied to MRS data distinguished normal liver tissue from HCC with an accuracy of 100%. Normal liver tissue was distinguished from cirrhotic liver with an accuracy of 92% and cirrhotic liver was distinguished from HCC with an accuracy of 98%. CONCLUSIONS: SCS applied to proton MRS of liver biopsies provides a robust method to distinguish, with a high degree of accuracy, HCC from both cirrhotic and normal liver.     

Proteomic profiling of hepatitis B virus-related hepatocellular carcinoma in China: a SELDI-TOF-MS study

Hepatocellular carcinoma (HCC) is one of the most common malignancies with high mortality, but its underlying molecular mechanisms remain not well understood. High-throughput, proteomic techniques targeting unique biological molecules may provide novel insights into HCC pathogenesis and prognosis. In this study, we systemically investigated tissue biomarkers of HCC by using surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technique. Proteomic spectra were generated from fresh tissues (26 HCC and 18 control cirrhotic liver) and analyzed by using Biomarker Wizard System. A total of 16 differential proteomic peaks were detected between HCC and cirrhotic liver tissues, and 11 between moderately and highly differentiated HCCs. The expression pattern of one proteomic peak was validated by immunohistochemistry. These molecules are potential candidate biomarkers for early diagnosis of and targeted therapy for HCC.     

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

Whole slide digital imaging technology enables researchers to study pathologists’ interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists’ actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.     

A ranklet-based image representation for mass classification in digital mammograms

Regions of interest (ROIs) found on breast radiographic images are classified as either tumoral mass or normal tissue by means of a support vector machine classifier. Classification features are the coefficients resulting from the specific image representation used to encode each ROI. Pixel and wavelet image representations have already been discussed in one of our previous works. To investigate the possibility of improving classification performances, a novel nonparametric, orientation-selective, and multiresolution image representation is developed and evaluated, namely a ranklet image representation. A dataset consisting of 1000 ROIs representing biopsy-proven tumoral masses (either benign or malignant) and 5000 ROIs representing normal breast tissue is used. ROIs are extracted from the digital database for screening mammography collected by the University of South Florida. Classification performances are evaluated using the area Az under the receiver operating characteristic curve. By achieving Az values of 0.978 +/- 0.003 and 90% sensitivity with a false positive fraction value of 4.5%, experiments demonstrate classification results higher than those reached by the previous image representations. In particular, the improvement on the Az value over that achieved by the wavelet image representation is statistically relevant with the two-tailed p value <0.0001. Besides, owing to the tolerance that the ranklet image representation reveals to variations in the ROIs' gray-level intensity histogram, this approach discloses to be robust also when tested on radiographic images having gray-level intensity histogram remarkably different from that used for training.     

Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound

A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.     

Multiple occurrence of borderline hepatocellular nodules in human cirrhotic livers: possible multicentric origin of hepatocellular carcinoma

Borderline hepatocellular nodules (BHN), atypical adenomatous hyperplasia, macroregenerative nodule type II or dysplastic nodules in the cirrhotic liver are considered to be important prcancerous lesions transforming to hepatocellular carcinoma (HCC). In order to evaluate the uni- or multicentric origin of BHN and HCC arising from BN, we surveyed 30 cirrhotic livers with BHNs that had been surgically resected or autopsied during 1973–1993. Among the 30 cirrhotic livers with BHNs, two or more BHNs were present in a single cirrhotic liver in 10 (33%) cases, while only one BHN was present in a single cirrhotic liver in the remaining 20 (67%) cases. The mean number of BHN in a cirrhotic liver with multiple BHNs was 3.5. Carcinomatous foci were present within BHN in 6 (60%) of the 10 cirrhotic livers with multiple BHNs, while they were present in 4 (20%) of the 20 cirrhotic livers with a single BHN; this difference was statistically significant (P<0.05). Coexistance of HCC was noted in 8 (80%) of the 10 cirrhotic livers with multiple BHNs, and in 3 (15%) of the 20 cirrhotic livers with a single BHN; this difference was statistically significant (P<0.01). There were no significant differences in age, sex, aetiology and morphology between cirrhotic livers with multiple BHNs and those with a single BHN. These data suggest that BHN and HCC arising from BHN may be of multicentric origin.     

Pattern of chromosomal imbalances in non-B virus related hepatocellular carcinoma detected by comparative genomic hybridization.

Only limited data are available on comparative genomic hybridization (CGH) in hepatocellular carcinoma (HCC). They concern mainly B virus related HCC. Therefore, we used CGH to detect chromosomal imbalances in 16 non-B virus related HCC in alcoholic cirrhosis in 7 cases (HA1 to HA7), in C virus cirrhosis in 7 cases (HC1 to HC7), in non-cirrhotic liver in 2 cases (NC1, NC2), and in 9 non-malignant cirrhotic tissues. The most frequent imbalances in HCC were gains of whole chromosomes or chromosomal regions 7 or 7q (10/16, 62%), 1q (9/16, 56%), 5 or 5q (9/16, 56%), 8q (8/16, 50%), 6p (6/16, 37%), 15q (5/16, 31%), 20 or 20q (5/16, 31%), and losses of 17p (6/16, 37%), and 8p (5/16, 31%). High-level gains were identified in HCC on 1q (2/16), 3q (1/16), 7q (1/16), and 8q (3/16). No chromosomal imbalances were detected in any of the cirrhotic tissues. Most of the gains, losses, and amplifications detected in this CGH study corresponded well to those identified in previous studies, except for gains of whole chromosome 5 or 7 and/or of chromosome arms 5q or 7q and losses on 4q. Our results suggest that other chromosomal regions are involved in hepatocarcinogenesis.     

Angiogenesis in liver cirrhosis and hepatocellular carcinoma

BACKGROUND: Angiogenesis has been well documented in hepatocellular carcinoma (HCC). As liver cirrhosis is considered preneoplastic condition, the aim of this study was to evaluate the process of angiogenesis using CD 34 as an endothelial cell marker in normal liver, cirrhosis and HCC. MATERIALS AND METHODS: A total of 111 cases were included in this study, which consisted of 30 cases each of normal liver and cirrhosis that were all autopsy cases. Twenty-one cases of HCC included 10 autopsy specimens, nine surgically resected specimens and two liver biopsies. Remaining were 30 cases of metastasis to the liver, which included 20 autopsy specimens, one surgically resected specimen and nine liver biopsies. The patients were between the age range from 17 to 80 years with 70 males and 11 females. Paraffin-embedded liver sections of all these cases were stained routinely by hematoxylin-eosin stain, while immunohistochemistry for CD 34 was performed for expression of endothelial cells. The positivity of CD 34 staining was evaluated by counting in 10 high-power field, grading was done from 0 to 4 and compared between normal liver, cirrhosis and HCC and metastasis. RESULTS: CD 34 was positive in 16/30 (53.3%) cases of cirrhosis, 18/21 (85%) cases of HCC and 26 (86.6%) of metastasis to the liver. None of the normal liver showed any positivity. Grade 3 to 4 positivity was seen in 4/16 (25%) and 13/18 (72%) cases of cirrhosis and HCC, respectively. Amongst these, 10 were moderately differentiated, one well differentiated and rest two were fibrolamellar and sarcomatoid variants of HCC. CONCLUSION: Over expression of endothelial cell marker CD 34 with gradual progression was found from normal liver to cirrhosis to HCC and metastasis. Understanding of this process of angiogenesis might help in the design of efficient and safe antiangiogenic therapy for these liver disorders.