The clinical motivation behind the work is straightforward but sobering. Breast cancer incidence and mortality have risen over the past decade worldwide, and survival improves dramatically when the disease is caught early. Mammography remains the mainstay imaging modality for early detection, and the standard medio-lateral oblique view captures three distinct regions in a single image: the breast region, the pectoral region, and the background. Radiologists, however, frequently face ambiguity when examining the pectoral region because both normal and abnormal masses and lymph nodes can appear there. Normal axillary lymph nodes are typically small and oval shaped, while abnormal ones tend to be dense, round, and larger than two centimeters. Because the parenchyma tissues of the breast and the pectoral muscle look so similar on a mammogram, computerized systems that leave the muscle in the frame often confuse the two, which is precisely why most algorithms simply remove the pectoral muscle and never look at it again.

The new framework refuses to throw that region away. Instead, it begins by suppressing the background of the image and isolating the pectoral muscle using a segmentation strategy built on position information, granular computing, rough set approximation, and an ant colony algorithm, refined in the team’s earlier research. Because these techniques produce only a discontinuous boundary on the CBIS-DDSM dataset, the authors added a line fitting step, essentially a simple linear regression that draws a straight line through the scattered boundary points to reconstruct the characteristic triangular contour of the muscle. The image is scanned from the top left corner, pixel coordinates are collected from the upper three-quarters of the frame, and the equation of a straight line is fitted to produce a clean, continuous segmentation. Once the pectoral muscle is isolated, the pipeline applies a three-by-three median filter to suppress noise, then enhances the contrast of the region using Contrast Limited Adaptive Histogram Equalization and a de-correlation stretch, both of which prepare the image for the more delicate steps that follow.

The enhancement matters because the pectoral muscle is composed of dense tissues with high intensity values, and masses and lymph nodes embedded within it often share those same high intensities with minimal contrast separating them from their surroundings. This ambiguity is where the fuzzy inference system enters. Rather than relying on classical edge detectors, which demand precise gradient calculations and struggle with low-contrast imagery, the fuzzy system embraces uncertainty. The researchers computed gradients in the x and y directions for the segmented muscle, convolved those gradient images with the enhanced image, and then passed the results through a fuzzy logic engine. Pixels were assigned to black and white fuzzy classes using triangular membership functions, chosen for their computational simplicity and their ability to capture smooth transitions between pixels, while Gaussian membership functions maximized intensities at edge regions. Two empirically constructed rules drive the decisions: if both x and y gradient values are zero, the output pixel receives maximum intensity, marking an edge; if either gradient is nonzero, the output receives minimum intensity. The result is a fuzzy edge image in which tissue boundaries inside the muscle stand out clearly despite the noise and imprecision inherent in mammographic data.

With the edges highlighted, the final detection stage hands the fuzzy edge image to the KAZE feature detector, a two-dimensional feature detection method that operates in a nonlinear scale space, in contrast to the linear Gaussian diffusion used by better-known detectors such as SIFT and SURF. KAZE constructs its scale space by solving a nonlinear diffusion equation in which a conductivity function, typically a Perona-Malik formulation, controls how strongly the diffusion acts at each location. This nonlinearity allows the algorithm to preserve well-defined boundaries of masses and lymph nodes, reduce noise, and retain distinctive contours without blurring away the very features that matter. Keypoints are detected by computing the scale-normalized determinant of the Hessian matrix from second-order spatial derivatives, and local maxima across both space and scale identify salient feature points. A rotationally invariant descriptor is then built for each keypoint by estimating its dominant orientation from first-order derivatives and aggregating weighted sums of those derivatives over local patches, normalized to unit length for contrast invariance.

Because the number and distribution of KAZE keypoints vary considerably across images, the researchers filtered them by response strength, retaining only the most significant ones. These strong keypoints reliably correspond to breast masses and axillary lymph nodes located near the pectoral muscle. The team validated the approach on the Curated Breast Imaging Subset of the Digital Database for Screening Mammography, a publicly available benchmark containing 2,620 images of which 292 medio-lateral oblique views contain masses, and on a private organization dataset of 240 medio-lateral oblique images. Ground truth segmentations were not supplied with the datasets for the pectoral muscle, so they were manually marked under the supervision of a consultant radiologist. Detection ground truths came from the dataset’s binary mass masks and, for the lymph nodes, from expert radiologist annotations provided as center-and-radius markings.

The quantitative results are striking. On the CBIS-DDSM dataset, segmentation of the pectoral muscle achieved 95.26 percent accuracy, while the private dataset yielded 92.11 percent. For detection, 38 images from CBIS-DDSM containing masses or lymph nodes in the pectoral region produced a detection accuracy of 97.36 percent, and the 27 relevant images from the private dataset produced 88.88 percent, for an average of roughly 93.12 percent across both. Measured against the total number of individual masses and nodes, accuracy was 86.04 percent on the public dataset and 70.58 percent on the private one. Because no false positives appeared in the detection results, precision reached 100 percent on both datasets, while recall reflected missed detections, yielding F1-scores of 92.50 percent for CBIS-DDSM and 82.80 percent for the private set. The detected locations were verified by comparing the centroid of each detection against its ground truth counterpart, with detections counted as correct when the coordinate differences stayed within a threshold factor of ten.

What makes these numbers particularly notable is the absence of any direct competitor. The authors report that a thorough search of the literature found no baseline algorithms for automatic detection of masses or axillary lymph nodes within the pectoral muscle, because nearly all prior abnormality-detection work deliberately excludes that region. Earlier pectoral muscle research focused almost entirely on removing the muscle cleanly, using methods ranging from thresholding and region growing to active contours, genetic algorithms, polynomial fitting, and single-sided edge marking. Mass detection studies, meanwhile, deployed region growing with neural networks, morphological thresholding, wavelet transforms, particle swarm optimization, and dual-stage adaptive thresholding, but always within the breast region proper. By turning the pectoral muscle from a discarded artifact into a region of clinical interest, the study opens a direction that the existing literature has barely touched, even though mammographic studies have long documented that abnormalities can reside there and that lymph node status is crucial for grading cancer and optimizing treatment.

The authors are candid about the current limitations. The method can detect only one mass and one axillary lymph node at a time, so some lesions are missed when multiple abnormalities share a frame, and the approach requires parameter readjustment to balance performance across datasets with different characteristics. Future work, they write, will involve larger datasets and an extension to multiple simultaneous detections. Even so, the pipeline demonstrates that a combination of classical segmentation, fuzzy edge reasoning, and nonlinear scale-space feature detection can surface clinically meaningful signs where conventional systems see nothing but tissue to be cropped away. If refined and validated at scale, such a tool could give radiologists an automated second look at the pectoral region, supporting earlier grading of breast cancer and helping to catch signs of spread before they are missed between the lines of a standard screening read.

Subject of Research: Automated detection of axillary lymph nodes and masses in the pectoral muscle region of mammograms using fuzzy logic and KAZE feature detection

Article Title: Detection of axillary lymph nodes and masses in pectoral region of mammograms using fuzzy logic and KAZE techniques

Article References: Divyashree, B. V., & Kumar, G. H. (2026). Detection of axillary lymph nodes and masses in pectoral region of mammograms using fuzzy logic and KAZE techniques. Discover Informatics, 1(1), Article 7. https://doi.org/10.1007/s44564-026-00006-1

Image Credits: AI Generated

DOI: 10.1007/s44564-026-00006-1

Keywords: mammography, breast cancer, axillary lymph nodes, pectoral muscle, fuzzy inference system, KAZE feature detector, computer-aided diagnosis, image segmentation, CBIS-DDSM, edge detection, CLAHE, medical imaging