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Huang W, Li N, Lin Z, Huang GB, Zong W, Zhou J, Duan Y. Liver tumor detection and segmentation using kernel-based Extreme Learning Machine. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:3662-3665. [PMID: 24110524 DOI: 10.1109/embc.2013.6610337] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper presents an approach to detection and segmentation of liver tumors in 3D computed tomography (CT) images. The automatic detection of tumor can be formulized as novelty detection or two-class classification issue. The method can also be used for tumor segmentation, where each voxel is to be assigned with a correct label, either a tumor class or nontumor class. A voxel is represented with a rich feature vector that distinguishes itself from voxels in different classes. A fast learning algorithm Extreme Learning Machine (ELM) is trained as a voxel classifier. In automatic liver tumor detection, we propose and show that ELM can be trained as a one-class classifier with only healthy liver samples in training. It results in a method of tumor detection based on novelty detection. We compare it with two-class ELM. To extract the boundary of a tumor, we adopt the semi-automatic approach by randomly selecting samples in 3D space within a limited region of interest (ROI) for classifier training. Our approach is validated on a group of patients' CT data and the experiment shows good detection and encouraging segmentation results.
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52
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Effect of Slice Thickness on Texture-Based Classification of Liver Dynamic CT Scans. COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT 2013. [DOI: 10.1007/978-3-642-40925-7_10] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/08/2022]
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53
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García G, Maiora J, Tapia A, De Blas M. Evaluation of texture for classification of abdominal aortic aneurysm after endovascular repair. J Digit Imaging 2012; 25:369-76. [PMID: 21901536 DOI: 10.1007/s10278-011-9417-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
Abstract
The use of the endovascular prostheses in abdominal aortic aneurysm has proven to be an effective technique to reduce the pressure and rupture risk of aneurysm. Nevertheless, in a long-term perspective, complications such as leaks inside the aneurysm sac (endoleaks) could appear causing a pressure elevation and increasing the danger of rupture consequently. At present, computed tomographic angiography (CTA) is the most common examination for medical surveillance. However, endoleak complications cannot always be detected by visual inspection on CTA scans. The investigation on new techniques to detect endoleaks and analyse their effects on treatment evolution is of great importance for endovascular aneurysm repair (EVAR) technique. The purpose of this work was to evaluate the capability of texture features obtained from the aneurysmatic thrombus CT images to discriminate different types of evolutions caused by endoleaks. The regions of interest (ROIs) from patients with different post-EVAR evolution were extracted by experienced radiologists. Three techniques were applied to each ROI to obtain texture parameters, namely the grey level co-occurrence matrix (GLCM), the grey level run length matrix (GLRLM) and the grey level difference method (GLDM). The results showed that GLCM, GLRLM and GLDM features presented a good discrimination ability to differentiate between favourable or unfavourable evolutions. GLCM was the most efficient in terms of classification accuracy (93.41% ± 0.024) followed by GLRLM (90.17% ± 0.077) and finally by GLDM (81.98% ± 0.045). According to the results, we can consider texture analysis as complementary information to classified abdominal aneurysm evolution after EVAR.
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Affiliation(s)
- Guillermo García
- University of the Basque Country, Systems Engineering and Automatic Department, Polytechnical University College, Plaza Europa 1, San Sebastian, Spain.
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54
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Extraction of lesion-partitioned features and retrieval of contrast-enhanced liver images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2012; 2012:972037. [PMID: 22988480 PMCID: PMC3439994 DOI: 10.1155/2012/972037] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2012] [Revised: 06/24/2012] [Accepted: 07/16/2012] [Indexed: 11/17/2022]
Abstract
The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT) scans. The algorithm includes mainly two processes: (1) distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2) representation using bag of visual words (BoW) based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.
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55
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CHEN ELIANG, CHUNG YINUNG, CHUNG PAUCHOO, TSAI HORNGMING, HUANG YISHIUAN. USING A FUZZY ENGINE AND COMPLETE SET OF FEATURES FOR HEPATIC DISEASES DIAGNOSIS: INTEGRATING CONTRAST AND NON-CONTRAST CT IMAGES. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2012. [DOI: 10.4015/s1016237201000200] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In the diagnosis of hepatic diseases, “Contrast-Enhanced Computerized Tomography” (CECT) and “Non-Contrast CT” (NCT) are usually simultaneously adopted. In this paper, a system consisting of a fuzzy diagnosis engine and a feature quantizer, which extracts hepatic features from CECT and NCT images, is proposed for assisting hepatic disease diagnosis. Compared with existing methods, this paper differs in two folds. First, a more complete feature set composed of not only lesion textures, but also lesion morphological structure and lesion contrast to normal tissues is used. These features are described through mathematical models built inside the feature quantizer and served as the input of fuzzy diagnosis engine. Second, because of the use of the fuzzy diagnosis engine, the system is intrinsically with the capability of storing rules and may infer and adapt its rules according to learning data. Furthermore, uncertainty associated with disease diagnosis can be appropriately taken into considerations. The system has been tested using 131 sets of image data, which are to be classified into 4 types of diseases: liver cyst, hepatoma, cavernous hemagioma and metastatic liver tumor. Experimental results indicate that among these test data 78% of them are accurately classified as one type, while the remaining 22% of data are classified as more than one types of diseases. Even so, within these 22% of multiple-classified data, the correct type is always included in the output in each test, showing a promise of the system.
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Affiliation(s)
- E-LIANG CHEN
- Department of Electrical Engineering, National Cheng Kung University, Taiwan
| | - YI-NUNG CHUNG
- Department of Electrical Engineering, Da-Yeh University, Taiwan
| | - PAU-CHOO CHUNG
- Department of Electrical Engineering, National Cheng Kung University, Taiwan
| | - HORNG-MING TSAI
- Department of Radiology, Medical College and Hospital, National Cheng Kung University, Taiwan, ROC
| | - YI-SHIUAN HUANG
- Department of Electrical Engineering, National Cheng Kung University, Taiwan
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56
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Mougiakakou S, Valavanis I, Nikita A, Nikita KS. Diagnostic Support Systems and Computational Intelligence. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that promise to increase the efficiency of physicians during their clinical practice. This chapter provides a brief overview of the use of computational intelligence methods in the design and development of DSSs aimed at the differential diagnosis of hepatic lesions from Computed Tomography (CT) images. Furthermore, examples of DSSs developed by our research team for supporting the diagnosis of focal liver lesions from non-enhanced CT images are presented.
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Affiliation(s)
| | | | - Alexandra Nikita
- University of Athens and Diagnostic Imaging Center for the Woman and Child, Greece
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57
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Bhosale S, Aphale A, Macwan I, Faezipour M, Bhosale P, Patra P. Computer assisted detection of liver neoplasm (CADLN). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:1510-1513. [PMID: 23366189 DOI: 10.1109/embc.2012.6346228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
To date, radiologists evaluate neoplasm images manually. Currently there is wide spread attention for developing image processing modules to detect and measure early stage neoplasm growth in liver. We report the fundamentals associated with the development of a multifunctional image processing algorithm useful to measure early growth of neoplasm and the volume of liver. Using CADLN, a radiologist will be able to compare computer generated volumetric data in serial imaging of the patients over time, that eventually will enable assessing progression or regression of neoplasm growth and help in treatment planning.
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58
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Germinal Center Texture Entropy as Possible Indicator of Humoral Immune Response: Immunophysiology Viewpoint. Mol Imaging Biol 2011; 14:534-40. [DOI: 10.1007/s11307-011-0531-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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59
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Muthu Rama Krishnan M, Shah P, Choudhary A, Chakraborty C, Paul RR, Ray AK. Textural characterization of histopathological images for oral sub-mucous fibrosis detection. Tissue Cell 2011; 43:318-30. [DOI: 10.1016/j.tice.2011.06.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2011] [Revised: 06/22/2011] [Accepted: 06/27/2011] [Indexed: 10/17/2022]
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60
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Sharma N, Aggarwal LM. Automated medical image segmentation techniques. J Med Phys 2011; 35:3-14. [PMID: 20177565 PMCID: PMC2825001 DOI: 10.4103/0971-6203.58777] [Citation(s) in RCA: 242] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2009] [Revised: 07/15/2009] [Accepted: 08/24/2009] [Indexed: 12/13/2022] Open
Abstract
Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis, clinical studies and treatment planning. This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images. The motive is to discuss the problems encountered in segmentation of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.
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Affiliation(s)
- Neeraj Sharma
- School of Biomedical Engineering, Institute of Technology, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221 005, UP, India
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61
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Jiang H, Feng R, Gao X. Level set based on signed pressure force function and its application in liver image segmentation. ACTA ACUST UNITED AC 2011. [DOI: 10.1007/s11859-011-0748-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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62
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SHI Z, He L. Current Status and Future Potential of Neural Networks Used for Medical Image Processing. ACTA ACUST UNITED AC 2011. [DOI: 10.4304/jmm.6.3.244-251] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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63
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Muthu Rama Krishnan M, Shah P, Chakraborty C, Ray AK. Statistical analysis of textural features for improved classification of oral histopathological images. J Med Syst 2010; 36:865-81. [PMID: 20703647 DOI: 10.1007/s10916-010-9550-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2010] [Accepted: 06/20/2010] [Indexed: 11/24/2022]
Abstract
The objective of this paper is to provide an improved technique, which can assist oncopathologists in correct screening of oral precancerous conditions specially oral submucous fibrosis (OSF) with significant accuracy on the basis of collagen fibres in the sub-epithelial connective tissue. The proposed scheme is composed of collagen fibres segmentation, its textural feature extraction and selection, screening perfomance enhancement under Gaussian transformation and finally classification. In this study, collagen fibres are segmented on R,G,B color channels using back-probagation neural network from 60 normal and 59 OSF histological images followed by histogram specification for reducing the stain intensity variation. Henceforth, textural features of collgen area are extracted using fractal approaches viz., differential box counting and brownian motion curve . Feature selection is done using Kullback-Leibler (KL) divergence criterion and the screening performance is evaluated based on various statistical tests to conform Gaussian nature. Here, the screening performance is enhanced under Gaussian transformation of the non-Gaussian features using hybrid distribution. Moreover, the routine screening is designed based on two statistical classifiers viz., Bayesian classification and support vector machines (SVM) to classify normal and OSF. It is observed that SVM with linear kernel function provides better classification accuracy (91.64%) as compared to Bayesian classifier. The addition of fractal features of collagen under Gaussian transformation improves Bayesian classifier's performance from 80.69% to 90.75%. Results are here studied and discussed.
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Affiliation(s)
- M Muthu Rama Krishnan
- School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, India
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64
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Chuan-Yu Chang, Yue-Fong Lei, Chin-Hsiao Tseng, Shyang-Rong Shih. Thyroid Segmentation and Volume Estimation in Ultrasound Images. IEEE Trans Biomed Eng 2010; 57:1348-57. [DOI: 10.1109/tbme.2010.2041003] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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65
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A knowledge-based technique for liver segmentation in CT data. Comput Med Imaging Graph 2009; 33:567-87. [DOI: 10.1016/j.compmedimag.2009.03.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2008] [Revised: 02/26/2009] [Accepted: 03/30/2009] [Indexed: 11/20/2022]
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66
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Guo D, Qiu T, Bian J, Kang W, Zhang L. A computer-aided diagnostic system to discriminate SPIO-enhanced magnetic resonance hepatocellular carcinoma by a neural network classifier. Comput Med Imaging Graph 2009; 33:588-92. [PMID: 19656655 DOI: 10.1016/j.compmedimag.2009.04.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2008] [Revised: 04/09/2009] [Accepted: 04/09/2009] [Indexed: 10/20/2022]
Abstract
In this paper, a computer-aided diagnostic (CAD) system for the classification of rat liver lesions from MR imaging is presented. The proposed system consists of two modules: the feature extraction and the classification modules. 40 rats are used for hepatocellular carcinoma (HCC) induction with Diethylnitrosamine via drinking water. After Resovist is administrated by tail vein the animals are scanned by a 1.5-T MR scanner with T2-weighted FRFSE sequence. SPIO-enhanced images of 106 nodules (RNs(:) 24, HCCs: 82) are acquired, and 161 regions of interest (ROIs) are taken from the MR images .Six parameters of texture characteristics including Angular Second Moment, Contrast, Correlation, Inverse Difference Moment, Entropy, and Variance of 161 ROIs are calculated and assessed by gray-level co-occurrence matrices, then fed into a BP neural network (NN) classifier to classify the liver tissue into two classes: cirrhosis and HCC. Difference of each texture parameter between cirrhosis and HCC group is significant. The accuracy of classification of HCC nodules from cirrhosis is 91.67%. It indicates the ANN classifier based on texture is effective for classifying HCC nodules from cirrhosis on rat SPIO-enhanced imaging.
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Affiliation(s)
- Dongmei Guo
- Department of Electronic Engineering, Dalian University of Technology, Dalian 116024, China
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67
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Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PMM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmüller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer HP, Nemeth G, Raicu DS, Rau AM, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE TRANSACTIONS ON MEDICAL IMAGING 2009; 28:1251-1265. [PMID: 19211338 DOI: 10.1109/tmi.2009.2013851] [Citation(s) in RCA: 503] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
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Affiliation(s)
- Tobias Heimann
- Division of Medical and Biological Informatics, German Cancer Research Center, 69121 Heidelberg, Germany.
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68
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Computer-aided image analysis of focal hepatic lesions in ultrasonography: preliminary results. ACTA ACUST UNITED AC 2009; 34:183-91. [PMID: 18386094 DOI: 10.1007/s00261-008-9383-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
PURPOSE To develop a computer-aided image analysis (CAIA) algorithm for analyzing US features of focal hepatic lesions and to correlate the feature values of CAIA with radiologists' grading. MATERIALS AND METHODS Two abdominal radiologists, blinded to the final diagnosis, independently evaluated sonographic images of 51 focal hepatic lesions in 47 patients: hemangiomas (n = 19), hepatic simple cysts or cystic lesions (n = 14), hepatocellular carcinoma (n = 11), metastases (n = 6), and focal fat deposition (n = 1). All images were graded using a 3- to 5-point scale, in terms of border (roundness, sharpness, and the presence of peripheral rim), texture (echogenicity, homogeneity, and internal artifact), posterior enhancement, and lesion conspicuity. Using a CAIA, texture and morphological parameters representing radiologists' subjective evaluations were extracted. Correlations between the radiologists and the CAIA for assessing parameters in corresponding categories were computed by means of weighted kappa statistics and Spearman correlation test. RESULTS A good agreement was achieved between CAIA and radiologists for grading echogenicity (weighted kappa = 0.675) and the presence of hyper- or hypoechoic rim (weighted kappa = 0.743). Several CAIA-derived features representing homogeneity of the lesions showed good correlations (correlation coefficient (gamma) = 0.603 approximately 0.641) with radiologists' grading (P < 0.05). For internal artifact (gamma = 0.469-0.490) and posterior enhancement (gamma = -0.516) of the cyst and lesion conspicuity (gamma = 0.410), a fair correlation between CAIA and radiologists was obtained (P < 0.05). However, parameters representing lesions' border such as sharpness (gamma = 0.252-0.299) and roundness (gamma = -0.134-0.163) showed no significant correlation (P > 0.05). CONCLUSION As a preliminary step in US computer-aided diagnosis for focal hepatic lesions, a CAIA algorithm was constructed with a good agreement and correlation with human observers in most US features. In addition, these features should be weighted highly when a computer-aided diagnosis for characterizing focal liver lesions on US is designed and developed.
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69
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Liver segmentation by intensity analysis and anatomical information in multi-slice CT images. Int J Comput Assist Radiol Surg 2009; 4:287-97. [PMID: 20033595 DOI: 10.1007/s11548-009-0293-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2008] [Accepted: 02/01/2009] [Indexed: 10/21/2022]
Abstract
PURPOSE Quantitative assessment and essentially segmentation of liver and its tumours are of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Moreover, segmentation of liver is the basis of further processing such as visualization, liver resection planning, and liver shape analysis. In this paper, we propose an algorithm to estimate an initial liver boundary. METHODS The proposed method consists of four steps as follows: first, we compute statistical parameters of liver's intensity range, associated with a large cross-section of liver CT image, utilizing expectation maximization (EM) algorithm. Second, by automatic extraction of ribs and segmentation of the heart, we define a ROI to confine the liver region for the next operations. Third, we propose a double thresholding approach to divide the liver intensity range into two overlapping ranges. In this case, based on a decision table, we label an object as a liver candidate or disregard it from the rest of the procedures. Finally, we employ an anatomical based rule to finalize a candidate as a liver tissue. In this case, we propose a color-map transformation scheme to convert the liver gray images into color images. In this way, we attempt to visually differentiate the liver from its surrounding tissues. RESULTS We have evaluated the techniques in the presence of 14 randomly selected local datasets as well as all datasets from the MICCAI 2007 Grand Challenge workshop database. For the local datasets, the average overlap error and average volume difference were of values of 15.3 and 2.8%, respectively. In the case of the MICCAI datasets, the above values were estimated as 20.3 and -4.5%, respectively. CONCLUSION The results reveal that the proposed technique is feasible to perform consistent initial liver borders. The boundary might be then employed in an 'Active Contour' algorithm to finalize the liver mask.
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70
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Classification of Hepatic Tissues from CT Images Based on Texture Features and Multiclass Support Vector Machines. ACTA ACUST UNITED AC 2009. [DOI: 10.1007/978-3-642-01510-6_43] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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71
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Valavanis IK, Mougiakakou SG, Nikita A, Nikita KS. Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images. ACTA ACUST UNITED AC 2008; 2007:3741-4. [PMID: 18002811 DOI: 10.1109/iembs.2007.4353145] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802+/-0.083) in the discrimination of hepatic tissue.
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Affiliation(s)
- Ioannis K Valavanis
- Faculty of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Str., 15780 Zographou, Greece.
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72
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Lee J, Kim N, Lee H, Seo JB, Won HJ, Shin YM, Shin YG, Kim SH. Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2007; 88:26-38. [PMID: 17719125 DOI: 10.1016/j.cmpb.2007.07.005] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2007] [Revised: 06/28/2007] [Accepted: 07/08/2007] [Indexed: 05/15/2023]
Abstract
Automatic liver segmentation is difficult because of the wide range of human variations in the shapes of the liver. In addition, nearby organs and tissues have similar intensity distributions to the liver, making the liver's boundaries ambiguous. In this study, we propose a fast and accurate liver segmentation method from contrast-enhanced computed tomography (CT) images. We apply the two-step seeded region growing (SRG) onto level-set speed images to define an approximate initial liver boundary. The first SRG efficiently divides a CT image into a set of discrete objects based on the gradient information and connectivity. The second SRG detects the objects belonging to the liver based on a 2.5-dimensional shape propagation, which models the segmented liver boundary of the slice immediately above or below the current slice by points being narrow-band, or local maxima of distance from the boundary. With such optimal estimation of the initial liver boundary, our method decreases the computation time by minimizing level-set propagation, which converges at the optimal position within a fixed iteration number. We utilize level-set speed images that have been generally used for level-set propagation to detect the initial liver boundary with the additional help of computationally inexpensive steps, which improves computational efficiency. Finally, a rolling ball algorithm is applied to refine the liver boundary more accurately. Our method was validated on 20 sets of abdominal CT scans and the results were compared with the manually segmented result. The average absolute volume error was 1.25+/-0.70%. The average processing time for segmenting one slice was 3.35 s, which is over 15 times faster than manual segmentation or the previously proposed technique. Our method could be used for liver transplantation planning, which requires a fast and accurate measurement of liver volume.
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Affiliation(s)
- Jeongjin Lee
- School of Electrical Engineering and Computer Science, Seoul National University, Shinlim 9-dong, Kwanak-gu, Seoul, Republic of Korea
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73
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Mougiakakou SG, Valavanis IK, Nikita A, Nikita KS. Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artif Intell Med 2007; 41:25-37. [PMID: 17624744 DOI: 10.1016/j.artmed.2007.05.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2007] [Revised: 05/16/2007] [Accepted: 05/22/2007] [Indexed: 12/23/2022]
Abstract
OBJECTIVES The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. MATERIALS AND METHODS Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. RESULTS The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. CONCLUSIONS The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.
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Affiliation(s)
- Stavroula G Mougiakakou
- National Technical University of Athens, Faculty of Electrical and Computer Engineering, Biomedical Simulations and Imaging Laboratory, 9 Heroon Polytechneiou Str., 15780 Zografou, Athens, Greece.
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Lee CC, Chen SH, Chiang YC. Classification of Liver Disease from CT Images Using a Support Vector Machine. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2007. [DOI: 10.20965/jaciii.2007.p0396] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We propose a classifier based on the support vector machine (SVM) for automatic classification in liver disease. The SVM, stemming from statistical learning theory, involves state-of-the-art machine learning. The classifier is a part of computer-aided diagnosis (CADx), which assists radiologists in accurately diagnosing liver disease. We formulate discriminating between cysts, hepatoma, cavernous hemangioma, and normal tissue as a supervised learning problem, and apply SVM to classifying the diseases using gray level and co-occurrence matrix features and region-based shape descriptors, calculated from regions of interest (ROIs), as input. Significant features of ROI enable us to simplify SVM input space and to feed the SVM representative information. By simplifying and clarifying the diagnosis process, we separate the classification of liver disease into hierarchical multiclass classification. We use the receiver operating characteristic (ROC) curve to evaluate diagnosis performance, demonstrating the classifier’s good performance.
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75
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Ying H, Zhou F, Shields A, Muzik O, Wu D, Heath E. A novel computerized approach to enhancing lung tumor detection in whole-body PET images. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2007; 2004:1589-92. [PMID: 17272003 DOI: 10.1109/iembs.2004.1403483] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Positron emission tomography (PET) is complimentary to other imaging modalities such as CT and MRI and provides a unique and effective means for detecting tumors in vivo through tissue metabolism measurement. At the majority of clinics, only the attenuation-corrected images are read by the physician for tumor diagnosis; the unconnected images are not examined, losing critically important information for a small portion of patients. We have developed a novel image processing method capable of automatically detecting and ranking tumor candidates in the lungs using the whole-body PET images. The intended utility is to visually prompt tumor candidates, assisting the physician to achieve better diagnosis, especially when the candidates appear to be subtle. The technique takes advantage of different information contents in the emission, corrected and uncorrected images. It processes the images three-dimensionally and the processing consists of segmentation, multi-thresholding with volume criterion, and heuristics-based tumor candidate ranking. This method is fast in computation and display and thus is suitable for real-time applications using high-end PCs. Our preliminary retrospective study involving nine patients has yielded promising results.
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Affiliation(s)
- Hao Ying
- Department of Electronics and Computer Engineering, Wayne State University, Detroit, MI 48202, USA
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76
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Deglint HJ, Rangayyan RM, Ayres FJ, Boag GS, Zuffo MK. Three-Dimensional Segmentation of the Tumor in Computed Tomographic Images of Neuroblastoma. J Digit Imaging 2006. [DOI: 10.1007/10278-006-0769-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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77
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Abstract
In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation.
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Affiliation(s)
- Daw-Tung Lin
- Department of Computer Science and Information Engineering, National Taipei University, Taiwan, ROC.
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78
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79
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Chen DR, Chang RF, Chen CJ, Ho MF, Kuo SJ, Chen ST, Hung SJ, Moon WK. Classification of breast ultrasound images using fractal feature. Clin Imaging 2005; 29:235-45. [PMID: 15967313 DOI: 10.1016/j.clinimag.2004.11.024] [Citation(s) in RCA: 66] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2004] [Revised: 10/10/2004] [Accepted: 11/02/2004] [Indexed: 01/02/2023]
Abstract
Fractal analyses have been applied successfully for the image compression, texture analysis, and texture image segmentation. The fractal dimension could be used to quantify the texture information. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Furthermore, a computer-aided diagnosis (CAD) system based on the fractal analysis is proposed to classify the breast lesions into two classes: benign and malignant. To improve the classification performances, the ultrasound images are preprocessed by using morphology operations and histogram equalization. Finally, the k-means classification method is used to classify benign tumors from malignant ones. The US breast image databases include only histologically confirmed cases: 110 malignant and 140 benign tumors, which were recorded. All the digital images were obtained prior to biopsy using by an ATL HDI 3000 system. The receiver operator characteristic (ROC) area index AZ is 0.9218, which represents the diagnostic performance.
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Affiliation(s)
- Dar-Ren Chen
- Department of General Surgery, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua 500, Taiwan.
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80
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Liu F, Zhao B, Kijewski PK, Wang L, Schwartz LH. Liver segmentation for CT images using GVF snake. Med Phys 2005; 32:3699-706. [PMID: 16475769 DOI: 10.1118/1.2132573] [Citation(s) in RCA: 102] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Accurate liver segmentation on computed tomography (CT) images is a challenging task especially at sites where surrounding tissues (e.g., stomach, kidney) have densities similar to that of the liver and lesions reside at the liver edges. We have developed a method for semiautomatic delineation of the liver contours on contrast-enhanced CT images. The method utilizes a snake algorithm with a gradient vector flow (GVF) field as its external force. To improve the performance of the GVF snake in the segmentation of the liver contour, an edge map was obtained with a Canny edge detector, followed by modifications using a liver template and a concavity removal algorithm. With the modified edge map, for which unwanted edges inside the liver were eliminated, the GVF field was computed and an initial liver contour was formed. The snake algorithm was then applied to obtain the actual liver contour. This algorithm was extended to segment the liver volume in a slice-by-slice fashion, where the result of the preceding slice constrained the segmentation of the adjacent slice. 551 two-dimensional liver images from 20 volumetric images with colorectal metastases spreading throughout the livers were delineated using this method, and also manually by a radiologist for evaluation. The difference ratio, which is defined as the percentage ratio of mismatching volume between the computer and the radiologist's results, ranged from 2.9% to 7.6% with a median value of 5.3%.
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Affiliation(s)
- Fan Liu
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, New York 10021, USA.
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81
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Hein E, Albrecht A, Melzer D, Steinhöfel K, Rogalla P, Hamm B, Taupitz M. Computer-assisted diagnosis of focal liver lesions on CT images evaluation of the Perceptron algorithm. Acad Radiol 2005; 12:1205-10. [PMID: 16112516 DOI: 10.1016/j.acra.2005.05.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2005] [Revised: 05/02/2005] [Accepted: 05/02/2005] [Indexed: 11/28/2022]
Abstract
RATIONALE AND OBJECTIVE The purpose of the study was to investigate a modified version of a so-called Perceptron algorithm in detecting focal liver lesions on CT scans. MATERIALS AND METHODS The modified Perceptron algorithm is based on simulated annealing with a logarithmic cooling schedule and was implemented on a standard workstation. The algorithm was trained with 400 normal and 400 pathologic CT scans of the liver. An additional 100 normal and 100 pathologic scans were then used to test the detection of pathology by the algorithm. The total of 1000 scans used in the study were selected from the portal venous phase of upper abdominal CT examinations performed in patients with normal findings or hypovascularized liver lesions. The pathologic scans contained 1 to 4 focal liver lesions. For the preliminary version of the algorithm used in this study, it was necessary to define regions of interest that were converted to a matrix of 119 x 119. RESULTS Training of the algorithm with 400 examples each of normal and abnormal findings took about 75 hours. Subsequently, the testing took several seconds for processing each scan. The diagnostic accuracy in discriminating scans with and without focal liver lesions achieved for the 200 test scans was approximately 99%. The error rate for pathologic and normal scans was comparable to results reported in the literature, which, however, were obtained for much smaller test sets. CONCLUSION The modified Perceptron algorithm has an accuracy of close to 99% in detecting pathology on CT scans of the liver showing either normal findings or hypovascularized focal liver lesions.
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Affiliation(s)
- Eike Hein
- Department of Radiology, Charité, Medizinische Fakultät, Humboldt-Universität zu Berlin, 10098 Berlin, Germany.
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82
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Bilello M, Gokturk SB, Desser T, Napel S, Jeffrey RB, Beaulieu CF. Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT. Med Phys 2005; 31:2584-93. [PMID: 15487741 DOI: 10.1118/1.1782674] [Citation(s) in RCA: 50] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
The objective of this work was to develop and validate algorithms for detection and classification of hypodense hepatic lesions, specifically cysts, hemangiomas, and metastases from CT scans in the portal venous phase of enhancement. Fifty-six CT sections from 51 patients were used as representative of common hypodense liver lesions, including 22 simple cysts, 11 hemangiomas, 22 metastases, and 1 image containing both a cyst and a hemangioma. The detection algorithm uses intensity-based histogram methods to find central lesions, followed by liver contour refinement to identify peripheral lesions. The classification algorithm operates on the focal lesions identified during detection, and includes shape-based segmentation, edge pixel weighting, and lesion texture filtering. Support vector machines are then used to perform a pair-wise lesion classification. For the detection algorithm, 80% lesion sensitivity was achieved at approximately 0.3 false positives (FP) per slice for central lesions, and 0.5 FP per slice for peripheral lesions, giving a total of 0.8 FP per section. For 90% sensitivity, the total number of FP rises to about 2.2 per section. The pair-wise classification yielded good discrimination between cysts and metastases (at 95% sensitivity for detection of metastases, only about 5% of cysts are incorrectly classified as metastases), perfect discrimination between hemangiomas and cysts, and was least accurate in discriminating between hemangiomas and metastases (at 90% sensitivity for detection of hemangiomas, about 28% of metastases were incorrectly classified as hemangiomas). Initial implementations of our algorithms are promising for automating liver lesion detection and classification.
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Affiliation(s)
- Michel Bilello
- Department of Computer Science, Stanford University, Stanford, California 94305, USA.
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84
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Ayres FJ, Zuffo MK, Rangayyan RM, Boag GS, Filho VO, Valente M. Estimation of the tissue composition of the tumour mass in neuroblastoma using segmented CT images. Med Biol Eng Comput 2004; 42:366-77. [PMID: 15191083 DOI: 10.1007/bf02344713] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Neuroblastoma is the most common extra-cranial, solid, malignant tumour in children. Advances in radiology have made possible the detection and staging of the disease. Nevertheless, there is no method available at present that can go beyond detection and qualitative analysis, towards quantitative assessment of the tissue composition of the primary tumour mass in neuroblastoma. Such quantitative analysis could provide important information and serve as a decision-support tool to the radiologist and the oncologist, result in better treatment and follow-up and even lead to the avoidance of delayed surgery. The problem investigated was the improvement of the analysis of the primary tumour mass, in patients with neuroblastoma, using X-ray computed tomography (CT) images. A methodology was proposed for the estimation of the tissue content of the mass: it comprised a Gaussian mixture model for estimation, from segmented CT images, of the tissue composition of the primary tumour. To demonstrate the potential of the method, the results are presented of its application to ten CT examinations of four patients. The method provides quantitative information, and it was observed that the tumour in one of the patients reduced from 523 cm3 to 81 cm3 in volume, with an increase in calcification from about 20% to about 88% of the tumour volume, in response to chemotherapy over a period of five months. Results indicate that the proposed technique may be of considerable value in assessing the response to therapy of patients with neuroblastoma.
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Affiliation(s)
- F J Ayres
- Department of Electrical & Computer Engineering, University of Calgary, Calgary, Alberta, Canada
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85
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Saitoh T, Tamura Y, Kaneko T. Automatic segmentation of liver region based on extracted blood vessels. ACTA ACUST UNITED AC 2004. [DOI: 10.1002/scj.10592] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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86
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Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita AS, Kelekis D. A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. ACTA ACUST UNITED AC 2003; 7:153-62. [PMID: 14518728 DOI: 10.1109/titb.2003.813793] [Citation(s) in RCA: 170] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.
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Affiliation(s)
- Miltiades Gletsos
- Laboratory of Biomedical Simulation and Imaging, Faculty of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece
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87
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Aisen AM, Broderick LS, Winer-Muram H, Brodley CE, Kak AC, Pavlopoulou C, Dy J, Shyu CR, Marchiori A. Automated storage and retrieval of thin-section CT images to assist diagnosis: system description and preliminary assessment. Radiology 2003; 228:265-70. [PMID: 12832587 DOI: 10.1148/radiol.2281020126] [Citation(s) in RCA: 66] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
A software system and database for computer-aided diagnosis with thin-section computed tomographic (CT) images of the chest was designed and implemented. When presented with an unknown query image, the system uses pattern recognition to retrieve visually similar images with known diagnoses from the database. A preliminary validation trial was conducted with 11 volunteers who were asked to select the best diagnosis for a series of test images, with and without software assistance. The percentage of correct answers increased from 29% to 62% with computer assistance. This finding suggests that this system may be useful for computer-assisted diagnosis.
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Affiliation(s)
- Alex M Aisen
- Department of Radiology, Indiana University School of Medicine, UH 0279, 550 N University Blvd, Indianapolis, Indiana 46202, USA.
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Masumoto J, Hori M, Sato Y, Murakami T, Johkoh T, Nakamura H, Tamura S. Automated liver segmentation using multislice CT images. ACTA ACUST UNITED AC 2003. [DOI: 10.1002/scj.10210] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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89
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Lee WL, Chen YC, Hsieh KS. Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform. IEEE TRANSACTIONS ON MEDICAL IMAGING 2003; 22:382-392. [PMID: 12760555 DOI: 10.1109/tmi.2003.809593] [Citation(s) in RCA: 49] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
This paper describes the feasibility of selecting fractal feature vector based on M-band wavelet transform to classify ultrasonic liver images-normal liver, cirrhosis, and hepatoma. The proposed feature extraction algorithm is based on the spatial-frequency decomposition and fractal geometry. Various classification algorithms based on respective texture measurements and filter banks are presented and tested. Classifications for the three sets of ultrasonic liver images reveal that the fractal feature vector based on M-band wavelet transform is trustworthy. A hierarchical classifier, which is based on the proposed feature extraction algorithm is at least 96.7% accurate in the distinction between normal and abnormal liver images and is at least 93.6% accurate in the distinction between cirrhosis and hepatoma liver images. Additionally, the criterion for feature selection is specified and employed for performance comparisons herein.
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Affiliation(s)
- Wen-Li Lee
- Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan 300, ROC
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Lee S, Lo C, Wang C, Chung P, Chang C, Yang C, Hsu P. A computer-aided design mammography screening system for detection and classification of microcalcifications. Int J Med Inform 2000; 60:29-57. [PMID: 10974640 DOI: 10.1016/s1386-5056(00)00067-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
This paper presents a prototype of a computer-aided design (CAD) diagnostic system for mammography screening to automatically detect and classify microcalcifications (MCCs) in mammograms. It comprises four modules. The first module, called the Mammogram Preprocessing Module, inputs and digitizes mammograms into 8-bit images of size 2048x2048, extracts the breast region from the background, enhances the extracted breast and stores the processed mammograms in a data base. Since only clustered MCCs are of interest in providing a sign of breast cancer, the second module, called the MCCs Finder Module, finds and locates suspicious areas of clustered MCCs, called regions of interest (ROIs). The third module, called the MCCs Detection Module, is a real time computer automated MCCs detection system that takes as inputs the ROIs provided by the MCCs Finder Module. It uses two different window sizes to automatically extract the microcalcifications from the ROIs. It begins with a large window of size 64x64 to quickly screen mammograms to find large calcified areas, this is followed by a smaller window of size 8x8 to extract tiny, isolated microcalcifications. Finally, the fourth module, called the MCCs Classification Module, classifies the detected clustered microcalcifications into five categories according to BI-RADS (Breast Imaging Reporting and Data System) format recommended by the American College of Radiology. One advantage of the designed system is that each module is a separate component that can be individually upgraded to improve the whole system. Despite that it is still is a prototype system a preliminary clinical evaluation at TaiChung Veterans General Hospital (TCVGH) has shown that the system is very flexible and can be integrated with the existing Picture Archiving and Communications System (PACS) currently implemented in the Department of Radiology at TCVGH.
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Affiliation(s)
- S Lee
- Department of Radiology, Taichung Veterans General Hospital, 40705, Taichung, Taiwan, ROC
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