Filtering of Mammograms Based on Convolution with Directional Fractal Masks to Enhance Microcalcifications
Source abstract
Abstract
The image processing of mammograms is very important for the early detection of breast pathologies, including cancer. This paper proposes a new technique based on directional fractal filtering for detecting microcalcification clusters or irregularly shaped microcalcifications. The proposed algorithm has two parts: a preprocessing step for detecting and locating microcalcification; and a second zooming, enhancement, and segmentation step. Detection is performed by image convolution using a set of masks with interesting fractal properties. Combined with other simple mathematical operations, remarkable contrast enhancement and segmentation are produced. The final result permits the clear delineation of the shape of individual microcalcifications. A comparison is made with other microcalcification enhancement techniques described in the literature.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Medical Image Segmentation Techniques · Image Retrieval and Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Signal Theory and Communications, Escuela Politécnica Superior, Universidad de Alcalá, Campus Universitario, Ctra. de Madrid a Barcelona km 33.600, 28805 Alcalá de Henares, Spain
Medicine Department, Autonoma University, Arzobispo Morcillo, 4, 28029 Madrid, Spain
References (35)
Show source-provided references
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