Image Processing Reference

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The area and perimeter of the nuclei are expressed in image pixels. All the remaining

parameters, except for the nuclei density index, are shape parameters. All of them are scalar

quantities and take decimal values in the range 0-3. Variation analysis selected the four

best discriminative parameters for specified histology evaluations as: area, nuclei density

index, elongateness coe
cient, and area to convex area ratio.

A training set, formed from half of the analysed CE images (about 10,000 cell nuclei), was

used in the classification phase based on cluster analysis. During this phase, three classes

of nuclei were found. The rule bank for nuclei classification was created using learning by

examples as described in Section 6.3.5. The second half of nuclei served as test set for the

system testing. For minimization of fuzzy rules we used Strategy 1 using soundness degree

defined by Eq. 6.34 for every generated rule. In this system, we define the following centroid

deffuzification formula to determine the output class for each input pattern:

C =
P
j=1

IF
R
j
C
R
j

P
j=1

(6.49)

IF
R
j

where N is the number of rules, C
R
j

is the class number generated by rule R
j
(C
R
j

=

µ
R
j
(x
(t)
) and µ
R
j
(x
(t)
) denotes the mem-

bership grade of t-th feature in the fuzzy regions that the j-th rule occupies.

Nuclei classification results using generated fuzzy rule base are shown in Fig. 6.9 in the

right column. The classification with clustering was linguistically described by patholo-

gist from abnormal (class 1) to normal nuclei (class 3). While classes 1 and 3 are easily

distinguishable, class 2 shall be considered as an intermediate nuclei category. Since it is

more similar to class 1 (pathological cases) than to class 3 (normal) it was considered as an

abnormal category. The number of nuclei belonging to class 1 on NE images was low (in

the range 0 to 3) and the area occupied by this type of nuclei was less than 1% of the total

nuclei area. Therefore, these nuclei were excluded from further analysis.

The last step was the verification of the hypothesis that amount of area occupied by

nuclei from every class is characteristic for each type of laryngeal lesion. The results of

variation analysis showed that the input parameters related to amount of area occupied

by every category of nuclei on analysed CE image originate from different distributions.

Mean values of area occupied by every category of nuclei in relation to specified histological

evaluations are presented in Fig. 6.6.

The most significant finding is a low number of nuclei of class 1 (most pathological) on

the NE images. This finding increases the diagnostic accuracy between malignant lesion

(SCC, SD) and precancerous or normal cases. Our results may also suggest that diagnosis

of carcinoma and severe dysplasia can definitely be made when the nuclei in class 1 (i.e.

highly pathological) cover more than 5% of the total nuclei area, and when nuclei in class

2 (i.e. moderately pathological) cover more that 40% of total nuclei area. Concluding, the

observations from our research have direct implications. Specifically, we have confirmed

that practical assessment of the nuclear morphometry for the diagnosis of laryngeal lesions

by contact endoscopy is possible and can be improved by fuzzy rule-based system. Another

advantage of contact endoscopy is that it can indicate appropriate tissue area for biopsy.

The method proposed herein may aid the clinician especially in the initial phase of the

learning curve as the valuable contribution.

= Π
4
t=1

0, 1, . . . , M ) and IF
R
j

is defined as IF
R
j

6.5

Conclusions

In general, in order to define a fuzzy rule-based system for image understanding tasks after

pre-processing, feature selection and extraction the following steps have to be taken:

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