Key Observations

Table 11.9 shows the shows the percentage improvement in segmenting the unenhanced image compared to that when segmenting the image enhanced using the predicted enhancement method for each of the two strategies compared that

Table 11.9: Mean percentage improvement in segmenting an unenhanced mammogram compared to that obtained when segmenting the image enhanced using the predicted enhancement method from each strategy for all breast types

Type

Mean TP

Mean SUBTP

Total

Target expert

1.00

2.00

1.20

FUZZY expert

0.11

4.25

0.79

DNM

0.08

1.16

0.24

(A) BPM FBP26

0.20

3.75

0.78

(B) BPM FBP3i6

0.13

3.66

0.72

Types 1-3 (A);

0.29

3.88

of the target optimal values from Table 11.4. Additionally, the table shows the result obtained by applying the FUZZY method to all images (given in Table 5(part c) over all four breast types. The last row in Table 11.9 shows the result of using the prediction from the BPM strategy with feature set FBP26 on breast types 1-3 and feature set FBP316 on type 4. From these results the following key observations are made:

1. Utility of contrast enhancement: From the complete dataset of mammograms, 75% showed an improved sensitivity following application of the expert contrast enhancement compared with the unenhanced original images.

2. Target experts: Figure 11.5 highlighted that given a set of contrast enhancement methods, different methods can be identified as target enhancement experts for different mammograms. This observation is the motivation for learning the optimal expert.

3. Characterizing a mammogram: Reviewing the results in Table 11.9, it can be seen that as the DNM strategy relies on characterizing a mammogram by a suspicious ROI, it performs poorly. In contrast the BPM strategy utilizes an image feature vector extracted from the breast comprizing an extensive set of features and performs better.

4. The superior BPM approach: The resultant performance using the modified BPM strategy based on breast type leads to a greater performance than simply using the FUZZY method. The result is inferior to the target contrast enhancement baseline performance indicating that learning the expert enhancement is a nontrivial problem. In implementing the modified BPM strategy, a mechanism of predicting the breast type is required.

5. Use of mammogram grouping knowledge: The BPM approach has been developed to utilize a priori knowledge describing the mammogram grouping indicating the mammographic breast density type. This knowledge is used to determine the feature extraction method to be used, either FBP26 for breast types 1-3 or FBP316 for type 4. In the experimental results presented above, the target breast type was used.

0 0

Post a comment