A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms

Figures

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  Figure 1. (a) Original X-ray coronary angiogram. (b) Ground truth of angiogram in (a). (c) Gaussian filter response applying 12 directional kernels on the angiogram in (a). (d), (e), (f) Gaussian templates with θ = 0°, θ = 45°, and θ = 90°, respectively, as proposed by Chaudhuri et al. [12].

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  Figure 2. Representation of an artificial neural network with one layer of m input values, a hidden layer with activation function g(·) and n neurons, and an output layer with one neuron and activation function h(·).

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  Figure 3. Flowchart of the proposed MGMF vessel detection method.

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  Figure 4. Search space to obtain the optimal architecture of the MGMF, including the number of neurons for each hidden layer of the ANN and using the area Az under the ROC curve in the training set of angiograms.

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  Figure 5. First row: subset of angiograms from the test set. Second row: ground truth of images. The remaining seven rows present the GMF response of the methods of GMF-Entropy [19], Kang et al. [18, 20, 21], Chaudhuri et al. [12], Cinsdikici and Aydin [16], Al-Rawi et al. [15, 22], GMF-Evol [23], and the proposed method (MGMF), respectively.

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  Figure 6. First row: subset of angiograms from the test set. Second row: ground truth of images. The remaining eight rows present the segmentation results of the proposed MGFM method, Kang et al. [21], Tsai et al. [10], Li et al. [9], Wang et al. [8], Eiho and Qian [1], Chanwimaluang et al. [13, 14], and Kang et al. [18], respectively.