Analysis of spiculation in the computerized classification of mammographic masses

Abstract

Spiculation is a primary sign of malignancy for masses detected by mammography. In this study, we developed a technique that analyzes patterns and quantifies the degree of spiculation present. Our current approach involves (1) automatic lesion extraction using region growing and (2) feature extraction using radial edge-gradient analysis. Two spiculation measures are obtained from an analysis of radial edge gradients. These measures are evaluated in four different neighborhoods about the extracted mammographic mass. The performance of each of the two measures of spiculation was tested on a database of 95 mammographic masses using ROC analysis that evaluates their individual ability to determine the likelihood of malignancy of a mass. The dependence of the performance of these measures on the choice of neighborhood was analyzed. We have found that it is only necessary to accurately extract an approximate outline of a mass lesion for the purposes of this analysis since the choice of a neighborhood that accommodates the thin spicules at the margin allows for the assessment of margin spiculation with the radial edge-gradient analysis technique. The two measures performed at their highest level when the surrounding periphery of the extracted region is used for feature extraction, yielding Az values of 0.83 and 0.85, respectively, for the determination of malignancy. These are similar to that achieved when a radiologist's ratings of spiculation (Az = 0.85) are used alone. The maximum value of one of the two spiculation measures (FWHM) from the four neighborhoods yielded an Az of 0.88 in the classification of mammographic mass lesions.

Improved mammographic interpretation of masses using computer-aided diagnosis

“…The mathematical model developed for spiculation extraction and analysis is based on pixel gray-level gradients within the mass. In contrast to algorithms based on border irregularity [23] or edge gradient [24], this method is independent of border selection. This allows for quantification of spiculation in ill-defined areas of increased density as well as in well-defined masses.…”

Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

“…Indis2-difference of the mean pixel values in the outside band and the inside band 3) Indis3-normalized radial gradient along the margin in the mass 4. Indis4-highest mean pixel value of gradient (Sobel) on four divided outlines of mass Indis1, Indis2, and Indis3 were often used for quantifying the degree of indistinctness in margins [10,[16][17][18][19][20]. Figure 3 shows an example of an inside and an outside band for margin of mass.…”

Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and ‐SNE

“…For a review of these techniques see Giger, Huo, Kupinski for x-ray mammography and Drukker for U.S., and Chen for DCE-MRI. [4][5][6][7][8][9][10][11]29 In each of the modalities, the lesion center is identified manually for the CADx algorithm, which then performs automated seeded segmentation of the lesion margin followed by computerized feature extraction. Table I below summarizes the content of the respective imaging modality databases used, including the total number of initial lesion features extracted.…”

“…3 A relatively well-developed clinical application, for which computerized efforts in radiological image analysis have been studied, is the use of CADx in the task of detecting and diagnosing breast cancer. [4][5][6][7][8][9][10] Similar to the radiologist's task, a computer algorithm is designed to make use of the highly complicated breast image input data, attempting to intelligently reduce image information into more interpretable and ultimately clinically actionable output structures, such as an estimate of the probability of malignancy. Understanding how to optimally make use of the enormity of the initial image information input and best arrive at the succinct conceptual notion of "diagnosis" is a formidable challenge.…”