# br from CC and other from MLO view

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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
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paired with every other lesion lying within the annular region
invariant to the positioning and the SQ 109 are chosen.
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of MLO of the same breast. Similarly, every lesion on MLO view
The features chosen based on an intuitive probability that they
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is paired with every other lesion lying within the annular
would extract useful information are explained briefly as
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region of CC of the same breast. To correlate the lesions on CC
below.
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and MLO views, a few similarity features with highest rank as
Pixelwise correlation (SF1): This similarity measure is based on
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explained in Ref. [46] are extracted from every possible pair of
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lesions. As experienced by the radiologists involved in this
Pearson's correlation coefficient. Pixelwise correlation of a
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study, the TP–TP pairs demonstrate dominant geometrical and
suspicious lesion Lcc on cc view with any suspicious lesion
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textural features than that of TP–FP or FP–FP pairs and hence
Lmlo on MLO view mammogram is defined using Eq. (8).
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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

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃqﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃððÞ

P

P

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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

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column of lesion template whereas
L
cc and
L
mlo are the mean
for this pair –

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pixel values of the lesion template of the suspicious lesions

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being evaluated, on CC and MLO view respectively.

ð

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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

P

where Ry is calculated using Eq. (1).

i;jðLmloði; jÞ LmloÞ

(iii) Correspondence score of this pair is calculated as

qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃqﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃððÞ

P

P

where Lcc
and Lmlo are the median pixel values of the lesion

ðRFði; jÞ þ SCði; jÞÞ

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template of the suspicious lesions being evaluated, on CC and

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MLO view respectively.

Similarly,
correspondence
score

Pixelwise mutual information (SF3): It is assumed that the

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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

log

information. Firstly, the highest correspondence score

ð ccð
Þ
mloð

ÞÞ

Lcc
ð
ð
Þ

ÞÞ

is assumed as belonging to a true positive lesion on the

XÞ

CC view. Secondly, the correspondence score for true

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where p denotes probability. The probabilities in Eq. (10) are

positive lesion on MLO view is calculated. The

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calculated based on a joint histogram of intensities of pixels

correspondence score can act as an extended feature

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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

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pixel levels of Lmlo(i,j) are taken along the y axis.

single and two view detection scheme covered in the

next section.

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