Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

Abstract

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures' results are less than 0.05, such as: relative overlap (p =0.0002), misclassification rate (p =0.045), true negative fraction (p =0.0001) and sum of true volume fraction (p =0.0001).

Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

“…However, the main disadvantage of the thresholding methods is the spatial incoherence (scattering) presented in segmented regions, as this method does not take into account pixels' neighborhood information. Region growing methods are an evolution, where segment's coherence is obtained from application of conditions by the user, such as homogeneity criteria between neighboring voxels and mostly the inclusion of manually induced seed pixels in the final segment [ 36 ]. Classification and clustering methods have been developed for classifying image pixels into different groups of similar intensities, thus properties, utilizing sophisticated algorithms such as k -NN, k -means, and fuzzy c -means [ 37 – 40 ].…”

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

“…In Table 2(Tab. 2) (References in Table 2: Al-Faris et al, 2014[6]; Kim et al, 2014[75]; Dheeba et al, 2014[34]; Al-Faris et al, 2014[7]; Hassanien et al, 2014[57]; Kannan et al, 2011[73]) a few examples of segmentation methods in breast CAD systems collected are shown. …”

Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna

“…Seeded region growing dilakukan sebagai proses segmentasi pada jaringan retina yang tipis (komplek) dan level set pada jaringan retina yang tebal. Dari penelitian tersebut bahwa seeded region growing dapat mengatasi permasalahan pada citra yang komplek, sedangkan pada penelitian [8] menggunakan seeded region growing untuk segmentasi citra mammogram. Pada penelitian [8] parameter seeded region growing didapatkan secara otomatis, berbeda dengan penelitian [7] yang parameternya didapatkan secara manual.…”