An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle

Abstract

In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. Its energy transition function is formulated based on global image information difference and local image information difference using different energy transfer strategies. First, an energy decrease strategy is used for modeling the spatial relation information of pixels. For modeling global image information difference, a seed information comparison function is developed using an energy preserve strategy. Then, a texture information comparison function is proposed for considering local image difference in different regions, which is helpful for handling blurry boundaries. Moreover, two neighborhood systems (von Neumann and Moore neighborhood systems) are integrated as the evolution environment, and a similarity-based criterion is used for suppressing noise and reducing computation complexity. The proposed method was applied to 205 clinical BUS images for studying its characteristic and functionality, and several overlapping area error metrics and statistical evaluation methods are utilized for evaluating its performance. The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.

Automated and real-time segmentation of suspicious breast masses using convolutional neural network

“…The majority of the algorithms use a seeded boundary, which is a rough estimate of the mass boundary drawn on a single B-mode frame or an initial point seed to initiate the segmentation algorithm. Some examples include, a leak plugging algorithm to find diffused and partially diffused boundaries based on a pre-specified seed [ 8 , 9 ], region-growing algorithms that grow regions based on an initial seed and eventually converge to the segmented boundaries [ 9 – 13 ], active contour model and its variations [ 14 – 16 ], a level set algorithm which uses the principle of active contour energy minimization [ 17 – 19 ], a two-stage active contour method based on an initial point seed [ 20 ], an automated particle swarm optimization clustering algorithm which does not require an initial seed but is computationally costly and not suitable for live imaging implementation [ 21 ], and a segmentation algorithm based on the cellular automata principle which requires an initial seed [ 22 ]. Marking a seed is a trivial task when reviewing cases retrospectively, but is a major impediment for segmentation during live imaging.…”

Research of the discrete orthogonal transformation received with use the dynamics of cellular automata

“…Введение В настоящее время известен ряд работ, в которых для решения задач цифровой обработки изображений используется математический аппарат теории клеточных автоматов. Данный аппарат применяется для улучшения изображений [1,2], сегментации [3,4], выделения контуров и распознавания текста [5][6][7][8][9], для построения схем разделения секрета, основанных на использовании цифровых изображений [10], а также в компьютерной стеганографии при встраивании в изображения цифровых водяных знаков [11,12].…”

Synthetic Brainbows

“…Cellular automata have been proposed as computational models for simulations in physics [Mar84] [RK88], material science [Bia94] [SBYR*91], and biology [HG93] [NM92]. They have also been extensively used in image segmentation algorithms [VK05] [KP08] [KP10] [KSH10] [GYXS11] [LCH*12]. However, most research focused on synchronous cellular automata, where the state of every cell is updated together.…”