Rotation-invariant feature extraction using a structural co-occurrence matrix

A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis

“…In this section, we present a brief description of the SCM, which is the basis for the approach of this article, as proposed by Bezerra Ramalho et al [ 25 ].…”

“…Bezerra Ramalho et al [ 25 ] introduced a general purpose structural image analytical method based on cooccurrence statistics saved in a matrix, namely, Structural Cooccurrence Matrix (SCM). This method analyzes, in an n -dimensional space, the relation between low-level structures of two discrete signals.…”

“…Here, k is configurable for each application. Thus, depending on the a priori knowledge of the characteristics of the image being analyzed, k can be either a high-pass filter or a low-pass filter [ 25 ].…”

“…The scalar attributes, obtained through the SCM method, are computed by the SCM generated between the input images. These attributes are divided into three groups: statistical group, information group, and divergent group [ 25 ]. All these attributes are computed based on the SCM matrix represented by M = m ij , and some are related to the marginal distribution P of the SCM.…”

Automated recognition of lung diseases in CT images based on the optimum-path forest classifier

“…The ACACM method was adopted here because it provides accurate segmentation results; indeed, it outperformed other segmentation methods available in the literature based on region growing, watershed, mathematical morphology and traditional active contour techniques [53,54], both in terms of processing time and accuracy. This method is based on an Active Contour Model, and encompasses computational intelligence techniques with prior knowledge about the lung anatomy [55,8,56,57]. Figure 1 depicts examples of lung segmentation results in thorax CT images acquired from patients with COPD and fibrosis, as well as from a healthy volunteer.…”