A voxel-based method for measuring sulcal width was developed, validated and applied to a database. This method (EDT-based LM) employs the 3D Euclidean Distance Transform (EDT) of the pial surface and a Local Maxima labeling algorithm. A computational phantom was designed to test method performance; results revealed the method's inaccuracy δ, to range between 0.1 and 0.5 voxels, for a width that varied between 1 and 7 voxels. Two morphological descriptors were computed to characterize each defined sulcus: mean sulcal width (MSW) and mean absolute deviation (MAD). The former is the average width for all available width measurements within the sulcus, and the latter is the deviation of these measurements. The EDT-based LM method was applied to the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution Magnetic Resonance (MR) images of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. AD causes significant gray matter loss; hence, some sulci were expected to broaden. Methodological results concurred with this hypothesis. After a Wilcoxon test, MSW was grater in the case of all sulci pertaining to AD patients, (p < 0.05, FDR corrected), whereas MAD showed significant differences in 8 sulci (p < 0.05, FDR corrected). This work presents a novel voxel-based method for measuring sulcal width and extracting descriptors to characterize and compare the sulci within and across subjects.

Objective. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection. Approach. The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO) (median patient age (mpa), 48 years [interquartile range (IQR), 41-54 years]) with radiological findings was retrospectively analyzed. All patients underwent breast biopsy and were not previously treated. External testing was performed using mammograms from the National Cancer Institute (INCAN) (mpa: 47 years [IQR, 37-62 years]). The morphology was analyzed using a circularity descriptor (), and the texture was analyzed using the mean height/width ratio of the extrema descriptor (). These results were compared with cancer/benign histopathology, which was binarily classified using ANNs. The F1-score, Cohen’s kappa (K), and area under the ROC curve (AUC) were employed as evaluation metrics, and the Wilcoxon rank-sum test was used for statistical analysis (h = 0, with p > 0.05, was considered as not statistically significant). Main results. 216 raw mammograms from HRAEO and 33 mammograms from INCAN (95+16 breast cancer and 121+17 benign findings) were included. The best internal testing results were obtained with a one-hidden-layer ANN with 100 neurons, achieving a F1-score of 0.95, K of 0.91, and an AUC of 0.953 (95% confidence interval [CI]: 0.917, 0.977) (h=0, p>0.99). However, the external testing results were significantly lower: 0.38 F1-score, 0.02 K, and 0.509 AUC (95% CI: 0.344, 0.664) (h=0, p=0.14) due to not exactly meeting the inclusion criteria and possible demographic and spectrum bias, or domain-adaptation issues. Significance. The proposed morphology () and texture () descriptors show promise for detecting breast cancer in raw mammograms, with radiological findings, in a local context. However, their poor external performance highlights the need for substantial further work before this approach can be deemed suitable for broader diagnostic applications.