A reinforcement agent for object segmentation in ultrasound images

Sahba, Farhang; Tizhoosh, Hamid R.; Salama, Magdy M

doi:10.1016/j.eswa.2007.07.057

Cited by 11 publications

(4 citation statements)

References 28 publications

(24 reference statements)

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“…Although RL agents have been used in some image processing tasks, according to the work in [11][12][13][14]16], the application of Q-learning in image segmentation has not been explored until recent years. We show that RL agent is suitable for medical image segmentation where several regions are processed simultaneously.…”

Section: Methodsmentioning

confidence: 99%

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Software Agent with Reinforcement Learning Approach for Medical Image Segmentation

Chitsaz¹,

Seng²

2011

J. Comput. Sci. Technol.

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Many image segmentation solutions are problem-based. Medical images have very similar grey level and texture among the interested objects. Therefore, medical image segmentation requires improvements although there have been researches done since the last few decades. We design a self-learning framework to extract several objects of interest simultaneously from Computed Tomography (CT) images. Our segmentation method has a learning phase that is based on reinforcement learning (RL) system. Each RL agent works on a particular sub-image of an input image to find a suitable value for each object in it. The RL system is define by state, action and reward. We defined some actions for each state in the sub-image. A reward function computes reward for each action of the RL agent. Finally, the valuable information, from discovering all states of the interest objects, will be stored in a Q-matrix and the final result can be applied in segmentation of similar images. The experimental results for cranial CT images demonstrated segmentation accuracy above 95%.

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Section: Methodsmentioning

confidence: 99%

“…Sahba et al [15][16] proposed an RL model to segment an ultrasound image of the prostate. First, the image is divided into some sub-images.…”

Section: Literature Reviewmentioning

confidence: 99%

Software Agent with Reinforcement Learning Approach for Medical Image Segmentation

Chitsaz¹,

Seng²

2011

J. Comput. Sci. Technol.

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“…An experienced operator guides the agents via the graphics user interface. References [56,57] propose an online RL evaluation. The US image is divided into sub-areas, and the actions are defined as adjusting the thresholds and the structural elements.…”

Section: Reinforcement Learning For Medical Image Segmentationmentioning

confidence: 99%

Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation

Karunanayake,

Moodleah,

Makhanov

2023

Diagnostics

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A segmentation model of the ultrasound (US) images of breast tumors based on virtual agents trained using reinforcement learning (RL) is proposed. The agents, living in the edge map, are able to avoid false boundaries, connect broken parts, and finally, accurately delineate the contour of the tumor. The agents move similarly to robots navigating in the unknown environment with the goal of maximizing the rewards. The individual agent does not know the goal of the entire population. However, since the robots communicate, the model is able to understand the global information and fit the irregular boundaries of complicated objects. Combining the RL with a neural network makes it possible to automatically learn and select the local features. In particular, the agents handle the edge leaks and artifacts typical for the US images. The proposed model outperforms 13 state-of-the-art algorithms, including selected deep learning models and their modifications.

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“…The parameters learned by RL were shown to be superior to the parameters previously recommended. Other applications of RL to learn parameters of image segmentation algorithms includes: contrast adaptation (Tizhoosh and Taylor, 2006), finding the appropriate threshold in order to convert an image to a binary one (Yin, 2002;Shokri and Tizhoosh, 2003Sahba et al, 2008) and detection of patterns in satellite images (Hossain et al, 1999).…”

Section: Reinforcement Learning and Its Applications In Computer Visionmentioning

confidence: 99%

Automatic Selection of Object Recognition Methods Using Reinforcement Learning

Bianchi

Ramisa

Mántaras

2010

Advances in Machine Learning I

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Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and intuition, not systematically taking into account information that can be found in the images and in the visual process itself to learn which algorithm should be used, in execution time. This paper presents a method that uses Reinforcement Learning to decide which algorithm should be used to recognize objects seen by a mobile robot in an indoor environment, based on simple attributes extracted on-line from the images, such as mean intensity and intensity deviation. Two state-of-the-art object recognition algorithms can be selected: the constellation method proposed by Lowe together with its interest point detector and descriptor, the Scale-Invariant Feature Transform and Nistér and Stewénius Vocabulary Tree approach. A set of empirical evaluations was conducted using a image database acquired with a mobile robot in an indoor environment, and results obtained shows that the approach adopted here is very promising.

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A reinforcement agent for object segmentation in ultrasound images

Cited by 11 publications

References 28 publications

Software Agent with Reinforcement Learning Approach for Medical Image Segmentation

Software Agent with Reinforcement Learning Approach for Medical Image Segmentation

Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation

Automatic Selection of Object Recognition Methods Using Reinforcement Learning

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