Kluwer - Handbook of Biomedical Image Analysis Vol
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Segmented blood vessels (left) |
Feature map (left) |
Segmented blood vessels (right) |
Feature map (right) |
Interpolated disparity map Disparity map
(a) Disparity map with old segmentation technique
Figure 6.19: Disparity maps generated with and without DA feature extraction.
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Segmented blood vessels (left) |
Feature map (left) |
Segmented blood vessels (right) |
Feature map (right) |
Interpolated disparity map |
Disparity map |
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(b) New DA-based segmentation technique |
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(c) Disparity map obtained from DA blood vessel segmentation
(d) Disparity map obtained from general edge detection for blood vessel segmentation
Figure 6.19: (cont.)
of cervical lesions. Automated image analysis is helpful in providing quantative lesion description thus monitoring of chronic lesions so that the onset of cervical cancer can be treated effectively.
A cervix image is a magnified color photograph of the cervix (illustrated in Fig. 6.20(a)). The acetowhite lesion area below the opening is marked by a trained physician, serving as a reference to other segmented images resulting from the algorithms. This image is taken with a regular high-resolution color camera, thus the most prominent problems preceding segmentation of the acetowhite lesion area from the rest are the reduction or removal of the glare and non-uniform illumination. Figure 6.20(b) shows the segmentation without illumination correction. All algorithms fail to recognize the lesion close to the cervix opening because the area is darker than other parts of the lesion, and vice versa for the section in the lower part of the cervigram, where the normal area is falsely classified as lesion. The glare on the top left of the lesion is also misclassified. Figures 6.20(c)–6.20(f) are the segmentation results from k-means, DA, and AFLC after illumination correction. The results are similar, with DA generating the closest partition to the manual segmentation.
6.5 Conclusions
From segmentation of the MR images, it can be observed that DA provides the best performance in terms of accuracy and stability among all discussed
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(a) Lesion |
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physician |
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image (four clusters) |
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(d) DA-segmented lesion |
(e) AFLC-segmented lesion |
(f ) AFLC-segmented lesion |
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Figure 6.20: Segmentation of cervical lesion.
clustering algorithms in several aspects. It is unsupervised; since theoretically it is designed to reach global minimum, the result is not biased by initialization. For the same type of image, the pseudotemperature reduction rate can be fixed, thus the segmentation process does not need parameter manipulation thereby yielding fully automated processing. Although the processing speed of DA is slower than other clustering algorithms, for small images such as the ones used above (217 × 181), the processing speed of DA is comparable to the other algorithms used. DA is also noise tolerant because of its statistical nature.
Both k-means and FCM, the well-known clustering algorithms, suffer from the initialization and local minimum problems. Cluster initialization is crucial in
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yielding satisfactory results. When not initialized properly, a clustering algorithm might be trapped in a local minimum, failing to proceed to the correct cluster. Our experimentations show that with random initialization, both k-means and FCM fail to generate the lesion clusters in MRI MS segmentation. AFLC is an automated and adaptive improvement over k-means and FCM by incorporating neural leader clustering and FCM. The performance is improved; however, similar problems are still encountered. Initialization is eliminated by selecting the first incoming sample as initial centroid, therefore, the outcome is sampleorder dependent. DA is the best candidate for medical image segmentation by an advanced clustering technique. It is not sensitive to parameter tuning, and initialization problem, and is noise tolerant and guaranteed to converge.
Advanced clustering techniques can provide general solutions for effective segmentation of a broad range of medical images. All segmentation examples presented in section 6.3 use image intensity as the single feature to clustering algorithms to demonstrate the efficiency of the algorithms. In real applications, local property or connectivity of adjacent pixel can be embedded into segmentation to achieve more accurate segmentation [66, 67].
6.6 Acknowledgments
This work has been partially supported by funds from the Advanced Technology Program (ATP) (Grant No. 003644-0280-1999), Technology Development and Transfer Program (TDT) (Grant No. 003644-0217-2001) of the state of Texas, Kestrel Corporation, the NEI Grant No. 1 R43 EY14090-01 and the NSF Grant EIA9980296. We acknowledge Young I. Kim, M.D., and Mary Lucy M. Pereira, M.D., of Young H. Kwon’s (M.D., Ph.D.) team from University of Iowa Hospitals and Clinics for their contributions to manual segmentation of the stereo optic disk images. The authors are grateful to Daron Ferris, M.D., from the Medical College of Georgia for providing us with the cervigram images from the Guanacaste Project, Costa Rica, sponsored by the National Cancer Institute of USA.
Questions
1.What is the structure of adaptive fuzzy leader clustering (AFLC)?
2.Does AFLC have to initialize like k-means? If not, why?
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3.How does AFLC dynamically adjust the number of clusters?
4.What is the difference between deterministic annealing (DA) and simulated annealing (SA)?
5.What is the DA cost function and what does it minimize?
6.What effect does the temperature reduction rate parameter have on DA clustering?
7.How does DA adjust the number of clusters?
8.What does mass-constrained DA mean?
9.What makes MS segmentation different from normal brain segmentation?
10.Judging from the examples given in the chapter, what are the performance differences among AFLC, DA, FCM, and k-means?
11.What is the limitation of clustering segmentation based on image intensity?
12.How is clustering in retinal optic disk/cup and blood vessel segmentation better than regular edge detection techniques?
13.Why is registration necessary in 3-D retinal disk/cup segmentation and how is it done?
14.How is the 3-D optic disk/cup map created?
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