Kluwer - Handbook of Biomedical Image Analysis Vol

(a) T11mm30_90.raw

Figure 6.5: Noisy MRI and the corresponding truth model for CSF, gray matter, and white matter.

The truth models are originally fuzzy models (Figs. 6.5(b)–6.5(d)). Since all results produced from the algorithms are hard clustering, the fuzzy truth models are converted into hard models by classifying a pixel to the category in which it has the largest pixel value.

Misclassification results (in Figs. 6.6–6.9) show that DA and AFLC perform better than k-means and FCM, demonstrating the effectiveness of the advanced algorithms in being more noisy tolerant.

6.4.1.2 Segmentation of Lesions in Multiple Sclerosis from MRI

Segmentation of MS lesions from simulated MRI: It is difficult to partition MS from T1-, T2-, or PD-weighted images because of the lack of intensity difference between MS lesion and other brain tissues, as is illustrated in Figs. 6.10(a)– 6.10(c). It can be expected that when a pattern recognition technique

Figure 6.6: Segmentation of noisy MR image by AFLC.

(d) Misclassification: 10.48% (e) Misclassification: 10.47% (f) Misclassification: 5.10%

Figure 6.7: Segmentation of noisy MR image by DA.

Figure 6.8: Segmentation of noisy MR image by k-means.

Figure 6.9: Segmentation of noisy MR image by FCM.

Figure 6.10: Segmentation of MS lesions.

resolution and reduced quality (with observable blocking artifacts.) The results of segmentations on these images are summarized in Figs. 6.11(e)–6.11(h), with labeling of the individual lesions shown in Fig. 6.11(e). Segmentation processes will be explained below. With image (a) and (b), intermediate results are shown in Figs. 6.12 and 6.13. For image (c) and (d), intermediate results are skipped and only the white matter masks and final segmentations are illustrated in Figs. 6.14 and 6.15.

Segmenting MS lesions from a single modality image such as the one used in this example is difficult as has been explained in the previous section. However, when images in other modalities are not available, background knowledge can be used. As most of the MS lesions occur in the white matter, Johnston et al.

[35] suggested creating a white mask to confine segmentation area such that segmentation accuracy can be enhanced. In this case, segmentation is a twopass process. The image is first roughly segmented into four categories with DA: the background, the white matter, gray matter, and CSF and other tissues (Figs. 6.12(b)–6.12(e)). Not surprisingly, the MS lesions cannot form a class of their own. They are classified either as gray matter or other categories. Then a white matter mask is generated by morphological filtering of the white matter. This is shown in Fig. 6.12(f). Using this mask, a tailored image containing only the masked area is obtained (Fig. 6.12(g)) and used as input image in the second pass DA clustering. Final segmentation for Fig. 6.12(g) is shown in Fig. 6.12(h). The result is superposed on the original image in Fig. 6.12(i).

Besides being affected by the image quality, the above segmentation is influenced by the mask. The misclassification of gray matter into MS lesion on the right-hand side of the image (indicated by the red arrow in Fig. 6.12) in this example is caused by misclassification of gray matter into the white matter mask.

One of the goals to segment the lesions is to provide quantitative analysis. Once the lesions are segmented and labeled, progress of each lesion in size can be obtained in chronic order. Table 6.1 summarizes the changes in size (number of pixels) of all lesions that exist through all four MR images.

6.4.2Retinal Image Segmentation from Stereo Fundus Images

Objects such as blood vessels, optic disk, and optic cup in retinal images are crucial in monitoring and detecting the progression of retinal diseases such as vascular diseases, glaucoma hypertension, and diabetic retinopathy.

Figure 6.12: DA segmentation of MS lesions from Fig. 6.11(a).

(i) Segmented lesion overlay on (a)

Figure 6.12: (cont.)

Table 6.1: Segmented lesion size in chronic MR images

a Lesions 4 and 5 merge in (c).

Figure 6.13: DA segmentation of MS lesions from Fig. 6.11(b).

Segmentation of the extracted features allows us to investigate the effect of occlusion induced by these features in generating stereo disparity mapping and 3-D visualization of the optic cup/disk, leading to more accurate diagnosis or monitoring of glaucoma. This is a challenging task due to poor and nonuniform illumination of most fundus images and lack of standardized parameters for stereo imaging geometry.

6.4.2.1 3-D Segmentation of the Optic Disk/Cup

The onset and progression of glaucoma can usually be found or monitored through the measurement of changes in the optic disk and optic cup area. It