br from CC and other from MLO view
550 from CC and other from MLO view. Every lesion on CC view is are easy to identify. In this regard, a set of features which are 560
551 paired with every other lesion lying within the annular region invariant to the positioning and the SQ 109 are chosen. 561
552 of MLO of the same breast. Similarly, every lesion on MLO view The features chosen based on an intuitive probability that they 562
553 is paired with every other lesion lying within the annular would extract useful information are explained briefly as 563
554 region of CC of the same breast. To correlate the lesions on CC below. 564
555 and MLO views, a few similarity features with highest rank as Pixelwise correlation (SF1): This similarity measure is based on 565
556 explained in Ref.  are extracted from every possible pair of 566
557 lesions. As experienced by the radiologists involved in this Pearson's correlation coefficient. Pixelwise correlation of a 567
558 study, the TP–TP pairs demonstrate dominant geometrical and suspicious lesion Lcc on cc view with any suspicious lesion 568
559 textural features than that of TP–FP or FP–FP pairs and hence Lmlo on MLO view mammogram is defined using Eq. (8). 569
Please cite this article in press as: Sapate S, et al. Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms.
is depicted in Fig. 4 and the same is explained with the help of
Pi;jðLccði; jÞ L2ccÞðLmloði; jÞ LmloÞ
two scenarios as below.
i;j ðLmloði; jÞ Lmlo Þ
5743 where Lcc(i,j) and Lmlo(i,j) are the pixel value at ith row jth (1) A lesion on CC view is paired with a lesion on MLO view and
575 column of lesion template whereas L cc and L mlo are the mean for this pair –
576 pixel values of the lesion template of the suspicious lesions
577 being evaluated, on CC and MLO view respectively.
58079 Correlation standardized by median (SF2): It is a variation of
Pearson's correlation coefficient and is defined using Eq. (9).
of the features.
(ii) Radial distance-based feature RF is calculated as
i;jðLccði; jÞ LccÞðLmloði; jÞ LmloÞ
where Ry is calculated using Eq. (1).
i;jðLmloði; jÞ LmloÞ
(iii) Correspondence score of this pair is calculated as
where Lcc and Lmlo are the median pixel values of the lesion
ðRFði; jÞ þ SCði; jÞÞ
586 template of the suspicious lesions being evaluated, on CC and
587 MLO view respectively.
Similarly, correspondence score
Pixelwise mutual information (SF3): It is assumed that the
5910 similar lesions contain a lot of intensity pixels which are
The pair with highest score is assumed to be a TP–
TP pair, rest all are false pairs.
redundant. The purpose of this information measure is to
maximize the redundant information between the partici-
(2) A lesion on CC view paired with no lesion on MLO view:
pating lesions Lcc(i,j) and Lmlo(i,j). The definition for the
the correspondence score is set to zero.
mutual information is given in Eq. (10).
Though the lesions from ipsilateral views are taken,
the aim here is to assess the suspiciousness of the
individual lesion on one (CC) view using its correlation
with lesion on the other (MLO) view as additional
pðLccði; jÞ; Lmloði; jÞÞ
information. Firstly, the highest correspondence score
ð ccð Þ mloð
Lcc ð ð Þ
is assumed as belonging to a true positive lesion on the
CC view. Secondly, the correspondence score for true
5998 where p denotes probability. The probabilities in Eq. (10) are
positive lesion on MLO view is calculated. The
600 calculated based on a joint histogram of intensities of pixels
correspondence score can act as an extended feature
601 levels of Lcc(i,j) taken along the x axis of the histogram and the
of that individual lesion. Hence, ingestive feeders is used in fusion of
602 pixel levels of Lmlo(i,j) are taken along the y axis.
single and two view detection scheme covered in the