Automated detection of the left ventricular region in gated nuclear cardiac imaging

Abstract

An approach to automated outlining the left ventricular contour and its bounded area in gated isotopic ventriculography is proposed. Its purpose is to determine the ejection fraction (EF), an important parameter for measuring cardiac function. The method uses a modified version of the fuzzy C-means (MFCM) algorithm and a labeling technique. The MFCM algorithm is applied to the end diastolic (ED) frame and then the (FCM) is applied to the remaining images in a "box" of interest. The MFCM generates a number of fuzzy clusters. Each cluster is a substructure of the heart (left ventricle,...). A cluster validity index to estimate the optimum clusters number present in image data point is used. This index takes account of the homogeneity in each cluster and is connected to the geometrical property of data set. The labeling is only performed to achieve the detection process in the ED frame. Since the left ventricle (LV) cluster has the greatest area of the cardiac images sequence in ED phase, a framing operation is performed to obtain, automatically, the "box" enclosing the LV cluster. THe EF assessed in 50 patients by the proposed method and a semi-automatic one, routinely used, are presented. A good correlation between the two methods EF values is obtained (R = 0.93). The LV contour found has been judged very satisfactory by a team of trained clinicians.

Multivariate image segmentation based on geometrically guided fuzzy C‐means clustering

“…In area B, u rc increases rapidly to higher values and the height of the step in the f rc line coincides with the range of threshold values as mentioned in the paragraph above. To verify that the optimal OutlierThreshold is not within the complete range of threshold values but is actually a single point, both experiments have been repeated for different noise levels (s = 5, 8, 12, 15), different kernel sizes (3,5,7) and different numbers of classes (2,3,4). All experiments showed that the OutlierThreshold value, when selected according to Equation (12), gives the best results.…”

Heart chambers and whole heart segmentation techniques: review

“…Clustering-based segmentation methods are considered to be an old yet robust technique. [51][52][53][54] One of the widely used clustering techniques is the K-means algorithm. This approach uses an objective function that expresses the performance of a representation for k given clusters.…”

Contour extraction of left ventricular cavity from digital subtraction angiograms using a neural edge detector

“…These include methods for US (ultrasonic) images [8][9][10][11][12], methods for X-ray CT (computed tomographic) images or MR (magnetic resonance) images [13][14][15][16][17], and methods for nuclear medical images [18][19][20]. These methods consist of detection of the edges of a contour and integration of the edges as a contour.…”