Ultrasound image segmentation: a survey <s> 4) Segmentation of the Myocardium and Epicardium: <s> A fully automated system for detecting the endocardial and epicardial boundaries in a two-dimensional echocardiography by using fuzzy reasoning techniques is proposed. The image is first enhanced by applying the Laplacian-of-Gaussian edge detector. Second, the center of the left ventricle is determined automatically by analyzing the original image. Next, a search process radiated from the estimated center is performed to locate the endocardial boundary by using the zero-crossing points. After this step, the estimation of the range of radius of a possible epicardial boundary is carried out by comparing the high-level knowledge of intensity changes along all directions with the actual image intensity changes. The high-level knowledge of global intensity change in the image is acquired from experts in advance, and is represented in the form of fuzzy linguistic descriptions and relations. Knowledge of local intensity change can therefore be deduced from the knowledge of global intensity change through fuzzy reasoning. > <s> BIB001 </s> Ultrasound image segmentation: a survey <s> 4) Segmentation of the Myocardium and Epicardium: <s> In this paper a temporal learning-filtering procedure is applied to refine the left ventricle (LV) boundary detected by an active-contour model. Instead of making prior assumptions about the LV shape or its motion, this information is incrementally gathered directly from the images and is exploited to achieve more coherent segmentation. A Hough transform technique is used to find an initial approximation of the object boundary at the first frame of the sequence. Then, an active-contour model is used in a coarse-to-fine framework, for the estimation of a noisy LV boundary. The PCA transform is applied to form a reduced ordered orthonormal basis of the LV deformations based on a sequence of noisy boundary observations. Then this basis Is used to constrain the motion of the active contour in subsequent frames, and thus provide more coherent identification. Results of epicardial boundary identification in B-mode images are presented. <s> BIB002 </s> Ultrasound image segmentation: a survey <s> 4) Segmentation of the Myocardium and Epicardium: <s> This study investigated the use of artificial neural networks (ANN) for image segmentation and spatial temporal contour linking for the detection of endocardial contours on echocardiographic images. Using a backpropagation network, the system was trained with 279 sample regions obtained from eight training images to segment images into either tissue or blood pool region. The ANN system was then applied to parasternal short axis images of 38 patients. Spatial temporal contour linking was performed on the segmented images to extract endocardial boarders. Left ventricular areas (end-systolic and end-diastolic) determined with the automated system were calculated and compared to results obtained by manual contour tracing performed by two independent investigators. In addition, ejection fractions (EF) were derived using the area-length method and compared with radionuclide ventriculography. Image quality was classified as good in 12 (32%), moderate in 13 (34%) and poor in 13 (34%) patients. The ANN system provided estimates of end-diastolic and end-systolic areas in 36 (89%) of echocardiograms, which correlated well with those obtained by manual tracing (R = 0.99, SEE = 1.44). A good agreement was also found for the comparison of EF between the ANN system and Tc-radionuclide ventriculography (RNV, R = 0.93, SEE = 6.36). The ANN system also performed well in the subset of patients with poor image quality. Endocardial contour detection using artificial neural networks and spatial temporal contour linking allows accurate calculations of ventricular areas from transthoracic echocardiograms and performs well even in images with poor quality. This system could greatly enhance the feasibility, accuracy and reproducibility of calculating cardiac areas to derive left ventricular volumes and ejection fractions. <s> BIB003 </s> Ultrasound image segmentation: a survey <s> 4) Segmentation of the Myocardium and Epicardium: <s> Echocardiographic quantitation of myocardial texture for diagnosis of early cardiomyopathy (CMP) remains problematic. Conventional statistical methods are limited, contributed by a small image region-of-interest (ROI) and difficulty in discrimination from noise. This study was performed to evaluate the 2-D Haar wavelet decomposition method as a tool to identify textural changes in a rat model of early CMP, focusing on changes that occur before development of M-mode structural abnormalities. Early diabetic CMP, ethanol CMP and diabetic-ethanol CMP rat models were evaluated. Echocardiography was performed on two groups of rats. Group I (5 week cohort, n = 4 per subgroup) included controls, rats on 12% ethanol, a diabetic subgroup, and diabetic rats on 4% ethanol. Group II (10 week cohort, n = 5 per subgroup) included the same categories as group I with an additional subgroup taking 4% ethanol was also studied. M-mode left ventricular measurements were comparable in all subgroups of group I. However, diabetic rats in group II had an increased left ventricular dimension (LVD) compared to all others and an increased septal dimension (IVSD) and posterior wall dimension (PWD) were noted in the 4% and 12% ethanol groups. End-diastolic digital images of all rats in the parasternal short axis view, at the papillary muscle level, were downloaded to a computer. A 16 x 16 (ROI) was selected from the anterior interventricular septum. Although standard statistical methods could not differentiate any of the groups, calculation of textural energy and normalized textural energy with the 2-D Haar wavelet decomposition method found at 5 weeks increased normalized texture energy in diabetics compared to all others. At 10 weeks increased texture energy was noted in diabetics. Diabetic-ethanol rats at both 5 and 10 weeks revealed a blunted textural energy compared to diabetic rats. In a rat model of diabetic cardiomyopathy, the 2-D wavelet decomposition method identified textural energy changes before development of echocardiographic structural changes. Ethanol-associated blunting of textural changes in diabetic rats was also noted. This method for quantitation of ventricular texture may be relevant for diagnosis of early cardiomyopathy. <s> BIB004 </s> Ultrasound image segmentation: a survey <s> 4) Segmentation of the Myocardium and Epicardium: <s> A novel extension of active appearance models (AAMs) for automated border detection in echocardiographic image sequences is reported. The active appearance motion model (AAMM) technique allows fully automated robust and time-continuous delineation of left ventricular (LV) endocardial contours over the full heart cycle with good results. Nonlinear intensity normalization was developed and employed to accommodate ultrasound-specific intensity distributions. The method was trained and tested on 16-frame phase-normalized transthoracic four-chamber sequences of 129 unselected infarct patients, split randomly into a training set (n = 65) and a test set (n = 64). Borders were compared to expert drawn endocardial contours. On the test set, fully automated AAMM performed well in 97% of the cases (average distance between manual and automatic landmark points was 3.3 mm, comparable to human interobserver variabilities). The ultrasound-specific intensity normalization proved to be of great value for good results in echocardiograms. The AAMM was significantly more accurate than an equivalent set of two-dimensional AAMs. <s> BIB005 </s> Ultrasound image segmentation: a survey <s> 4) Segmentation of the Myocardium and Epicardium: <s> A model-based method for three-dimensional image segmentation was developed and its performance assessed in segmentation of volumetric cardiac magnetic resonance (MR) images and echocardiographic temporal image sequences. Comprehensive design of a three-dimensional (3-D) active appearance model (AAM) is reported for the first time as an involved extension of the AAM framework introduced by Cootes et al. The model's behavior is learned from manually traced segmentation examples during an automated training stage. Information about shape and image appearance of the cardiac structures is contained in a single model. This ensures a spatially and/or temporally consistent segmentation of three-dimensional cardiac images. The clinical potential of the 3-D AAM is demonstrated in short-axis cardiac MR images and four-chamber echocardiographic sequences. The method's performance was assessed by comparison with manually identified independent standards in 56 clinical MR and 64 clinical echo image sequences. The AAM method showed good agreement with the independent standard using quantitative indexes of border positioning errors, endo- and epicardial volumes, and left ventricular mass. In MR, the endocardial volumes, epicardial volumes, and left ventricular wall mass correlation coefficients between manual and AAM were R/sup 2/=0.94,0.97,0.82, respectively. For echocardiographic analysis, the area correlation was R/sup 2/=0.79. The AAM method shows high promise for successful application to MR and echocardiographic image analysis in a clinical setting. <s> BIB006
Finally, there is a very limited literature explicitly looking at segmentation and analysis of the myocardium BIB004 and epicardial border detection  , BIB001 , BIB002 . These are both challenging to do from native -mode images. There is also very little literature specifically focussed on getting methods to work on general clinical data rather than data from subjects with a good acoustic window i.e., which give good images. We have highlighted some of the few works that have done so above BIB005 , BIB003 , BIB006 . One further exception is the work of Boukerroui et al.  which considers how to enhance -mode images to reduce the affect of attenuation and enhance features. Although that method was shown quantitatively to reduce attenuation, enhance features, and not introduce artefacts after enhancement, that approach has not to-date been fully tested in clinical practice.