Cancer tissues recognition system using box counting method and artificial neural network

Abstract

The research presented in this paper was aimed to develop a recognition system for microscopic images of breast tissues samples. The system should classify breast tissues as malignant or not, or identifying their malignancy types. In this paper, multi-scale fractal dimension concept was used to extract a set of textural features in order to perform texture analysis for breast tissues samples. The box counting method was used to estimate the multi fractal dimensions. A feed forward neural network was used to classify different types of breast tissues according to the extracted fractal dimension vectors. For ANN training purpose the back-propagation training algorithm was used. Evaluation tests were carried on 368 breast tissues images. The test results indicated that the best attained success rate was around 97%.

Proposed Classification System by Using Artificial Neural Network

“…In both layers the number of neurons in each of these hidden layers must be carefully considered. Table (5) shows the effect of the number of hidden nodes on system success rate and learning time of the ANN respectively.…”

Application of Fractal and Euclidean Methods to Differentiate Normal and Neoplastic Thyroid Cells

“…[7] Fractal irregular objects that are the base of fractal geometry [8,9] and its notions have been applied for the characterization of irregular structures of the human body, revealing the possibility of developing more precise measures of the irregularity of these structures. [10][11][12][13][14][15][16][17][18][19][20][21] Among the designed methods to evaluate the degree of irregularity of fractal objects, the Box-Counting method is found. This method evaluates fractal dimension of fractal objects called wild fractals [8,9] such as coronary arteries [14] and has been applied to clinically characterize and diagnose glaucoma, the normal human vasculature, breast cancer tissues, and others.…”

“…This method evaluates fractal dimension of fractal objects called wild fractals [8,9] such as coronary arteries [14] and has been applied to clinically characterize and diagnose glaucoma, the normal human vasculature, breast cancer tissues, and others. [18][19][20] From this line of investigation, different diagnostic methodologies of clinical application that evaluate the different lesions of cervical cells have been developed capable of differentiating benign from malignant atypical squamous cells of undetermined significance relying on the occupied spaces of the nucleus and cytoplasm. [16] Considering that thyroid cells exhibit irregular features suggesting their fractality and in the frame of previous research, the purpose of this study is to conduct an application of a methodology developed by Rodríguez with the purpose of characterizing the irregularity of the nucleus and cytoplasm of cells obtained from biopsy samples of normal and benign and malignant papillary and follicular thyroid neoplasms through the box-counting method and to compare the values of the occupied spaces of the surface and border of these structures.…”

Synthesis of Mammographic Images Based on the Fractional Brownian Motion

“…In the literature, several approaches were proposed to segment microcalcifications [15][16][17][18] such as active contours [16,19], curvelet moments [20], wavelet analysis [21][22][23], fractal analysis [24][25][26], multifractal analysis [27,28] and morphological filters [29][30][31][32] in order to reduce human subjectivity in diagnosis.…”