br patterns incorporate a recursive process
337 patterns incorporate a recursive process to determine the
338 neighbouring pixels. However, the recursive algorithms are
339 inefficient as far as time and computational power are
340 concerned. To overcome this pitfall, a novel scheme called
341 ‘‘Sapate's neighbouring pixels selection scheme (SNPSS)’’ for
342 selecting unique pixels along eight radial directions is
344 The literature surveyed reveals that the tumour starts with
345 a tiny core and slowly starts growing in a radial pattern. This
346 type of biological tumour growth pattern has motivated us to
347 come out with SNPSS for modifying region growing method.
348 SNPSS allows us to get rid of the curse of recursive expansion
349 of region growing algorithm. The selection of neighbouring
350 pixels minimizes the repetition of the pixels which results in
351 computational effectiveness and requires no recursion at all.
Thus, segmentation using SNPSS plays a very important role in
352
delineating the exact boundary of the mass or tumour which is
353
crucial in further characterization and treatment planning
354
[39]. The results of mammogram segmentation are depicted in
355
Fig. 2(a) and (c).
356
The segmented images with multiple candidate lesions
357
undergo one more stage to reduce the false positive lesions.
358
The proposed system filters out some of the candidate lesions
359
using few empirical rules. Empirical rules are usually used for
360
predicting some data which have no numeric expression. This
361
happens when it CCCP uncoupler is very difficult to obtain right data which
362
follows normal distribution. Empirical rules applied to any
363
random variable can give rough estimate of what your data
364
collection may look like if you were able to survey the large
365
population. In our study, these parameters include area less
366
than 2500 pixels, extent smaller than 0.15, perimeter twice that
367
of image width and eccentricity greater than 0.98. All
368
parameters and their respective values are selected under
369
the guidance of the expert radiologists involved in this study.
370
While applying the empirical rule to any of above parameters,
371
the condition that 'approximately 68% of the data falls within
372
one standard deviation of the mean' is confirmed to be true.
373
The experimental results obtained are shown in Fig. 2(b)
374
and (d). Another case with multiple lesions is shown in Fig. 3.
375
The lesions in Fig. 3(b) are paired with lesions on MLO view in
376
Fig. 3(d) using a procedure described in Section 4.3.2. Seven
377
selected geometric features concerning shape, size, margin,
378
contrast and six textural features along 3 distances, 4
379
directions based on GLCM are calculated to characterize and
380
classify the detected lesions. The features selected have
381
shown highest classification accuracy. A pool of actively
382
discriminative geometric and textural features is fed to the
383
simple SVM classifier to achieve the classification results. The
384
Please cite this article in press as: Sapate S, et al. Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms. Biocybern Biomed Eng (2019), https://doi.org/10.1016/j.bbe.2019.04.008
Fig. 3 – FP reduction using empirical rules during the initial detection stage: (a) CC view with suspicious lesion detected; (b) multiple selected lesions after applying empirical rules for FP reduction; (c) MLO view of the same breast; (d) two lesions selected after FP reduction.
385
pixels in the background of the suspicious lesion do not play a
The average of the accuracy achieved for each fold is taken as a
414
386
vital role in characterizing the mass or tumour; hence the ROI
final result of the classifier.
415
387
is taken excluding the background. The textural and geometric
388
features extracted are enlisted and defined in Appendix A and
4.3.
Ipsilateral-view diagnosis
416
389
Appendix B respectively.
390
Support Vector Machine (SVM) is the simple, most popular
The overall design of proposed CAD system using ipsilateral
417
391
yet strong classifier for a small set of images [41]. It focuses
views is depicted in Fig. 4. The scheme generates a
418
392
more on confidence of classification to discriminate the
correspondence score for each suspicious lesion from two
419
393
benign mass and malignant tumours using the respective
views. The lesion is localized and confirmed as benign mass or
420
394
feature vectors as input. The decision function is defined as
malignant tumour as a final result which is achieved with four
421
395
Eq. (4):
steps: (1) The suspicious lesions on both views are located
422
using arc-based method and are paired using width of arc pair,