  Breast cancer is a health problem that affects mainly the female population.
An early detection increases the chances of effective treatment, improving the
prognosis of the disease. In this regard, computational tools have been
proposed to assist the specialist in interpreting the breast digital image
exam, providing features for detecting and diagnosing tumors and cancerous
cells. Nonetheless, detecting tumors with a high sensitivity rate and reducing
the false positives rate is still challenging. Texture descriptors have been
quite popular in medical image analysis, particularly in histopathologic images
(HI), due to the variability of both the texture found in such images and the
tissue appearance due to irregularity in the staining process. Such variability
may exist depending on differences in staining protocol such as fixation,
inconsistency in the staining condition, and reagents, either between
laboratories or in the same laboratory. Textural feature extraction for
quantifying HI information in a discriminant way is challenging given the
distribution of intrinsic properties of such images forms a non-deterministic
complex system. This paper proposes a method for characterizing texture across
HIs with a considerable success rate. By employing ecological diversity
measures and discrete wavelet transform, it is possible to quantify the
intrinsic properties of such images with promising accuracy on two HI datasets
compared with state-of-the-art methods.
