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Figure 15.16 3D projection of pixel brightness.
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6.1 Experimental Results
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The data collection that was used in our experiments was taken from the Mammographic Image Analysis Society [22]. This same collection has been used in other studies of automatic mammography classification. Its corpus consists of 322 images, which belong to three big categories: normal, benign and malign. There are 208 normal images, 63 benign and 51 malign, which are considered abnormal. In addition, the abnormal cases are further divided into six categories: microcalcification, circumscribed masses, spiculated masses, ill-defined masses, architectural distortion and asymmetry. All the images also include the locations of any abnormalities that may be present. The existing data in the collection consists of the location of the abnormality (like the center of a circle surrounding the tumor), its radius, breast position (left or right), type of breast tissue (fatty, fatty glandular and dense) and tumor type if it exists (benign or malign). All the mammograms are medio-lateral oblique views. Figure 15.17 shows experiments conducted on abnormal breast cases from that database with a threshold value fixed at 12 and the number of transition elements equal to 25. Actually, we noted that with higher transition elements the edges become sharper and more obvious. However, selecting n = 25 resulted in rather more moderate results than among the other odd values that we may have chosen for n. The DFC results are produced without using any preprocessing to increase the contrast or to reduce the image noise. Figure 15.17 shows two other notable edge detection methods applied on the same original image. We can furthermore reduce noise, as well as nonsignificant details, by applying a smooth filter before the DFC operation, see Figure 15.18. Moreover, it is a well-known medical fact that dense breast tissues on mammograms indicate a higher cancer risk [31]. A dense region in any mammogram always has high brightness values. For this reason, we visualized low-brightness edges with different colors than those edges of high brightness. In this direction, we used blue to identify normal region edges (i.e. no possibility of having cancerous tissues, or 0 % density type in radiologist terminology). Green has been used to identify those edges with more dense regions indicating a higher possibility of having cancerous tissues (50 74 % density type). Finally, White is used to identify a very high possibility of having cancerous tissues (75 100 %
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Figure 15.17 DFC method applied to two mammograms. (a) The original image of Case #1, circumscribed cancer; (b) DFC threshold = 12 n = 25; (c) DFC with threshold = 12 n = 25 with coloring; (d) the original image of Case #2, stellate cancer; (e) DFC with threshold = 12 n = 25; (f) DFC with threshold = 12 n = 25 with coloring.
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Recognizing ROIs in Medical Images
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Figure 15.17 (continued)
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Figure 15.18 Effect of the DFC method with smooth filters. (a) Applying median blur before DFC; (b) applying Gaussian blur before DFC; (c) applying a morphology erosion before DFC.
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Figure 15.18 (continued)
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density type). We can consider this colorization process an additional rule which is based directly on the detected edge pixel brightness. Indeed, we tested DFC on all the normal cases of the database, none of them revealed specious structures with white or green colors. We are not claiming that the colorization scheme can be used to classify mammograms, but we can use it as one associative rule for mining abnormal mammograms. This issue is our current research program, in which we are trying to construct a data mining technique based on association rules extracted from our DFC method for categorizing mammograms. We are also intending, for the purpose of mammogram categorization, to use other measures besides our DFC association rules, like the brightness, mean, variance, skewness and kurtosis for the DFC segmented image. These measures have been reported to have some success in identifying abnormal mammograms [32]. Moreover, we experimented with all the edge detection techniques used in Table 15.1 and proved that no single method can be as effective as our current DFC method [33]. Figure 15.19 illustrates some comparisons for the stellate cancer image of this article.
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Table 15.1 Traditional mammography techniques. Mammography method Gray-level thresholding Comparing left and right breasts Compass filters Laplacian transform Gaussian filters Texture and fractal texture model A space scale approach Wavelet techniques Mathematical morphology Median filtering Box-rim method Reference [23] [24] [25] [11] [12] [17,19] [26] [27] [28] [29] [30]
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