Farhang Sahba

A coarse-to-fine approach to prostate boundary segmentation in ultrasound images

BackgroundIn this paper a novel method for prostate segmentation in transrectal ultrasound images is presented.MethodsA segmentation procedure consisting of four main stages is proposed. In the first stage, a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation.ResultsA total average similarity of 98.76%(± 0.68) with gold standards was achieved.ConclusionThe proposed approach represents a robust and accurate approach to prostate segmentation.

Application of Opposition-Based Reinforcement Learning in Image Segmentation

In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropriate local values and segment the object. The agent uses an image and its manually segmented version and takes some actions to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal as reward/punishment. The agent uses this information to explore/exploit the solution space. The values obtained can be used as valuable knowledge to fill the Q-matrix. The results demonstrate potential for applying this new method in the field of medical image segmentation

Quasi-global oppositional fuzzy thresholding

Opposition-based computing is the paradigm for incorporating entities along with their opposites within the search, optimization and learning mechanisms. In this work, we introduce the notion of “opposite fuzzy sets” in order to use the entropy difference between a fuzzy set and its opposite to carry out object discrimination in digital images. A quasi-global scheme is used to execute the calculations, which can be employed by any other existing thresholding technique. Results for prostate ultrasound images have been provided to verify the performance whereas experts markings have been used as gold standard.

Application of reinforcement learning for segmentation of transrectal ultrasound images

BackgroundAmong different medical image modalities, ultrasound imaging has a very widespread clinical use. But, due to some factors, such as poor image contrast, noise and missing or diffuse boundaries, the ultrasound images are inherently difficult to segment. An important application is estimation of the location and volume of the prostate in transrectal ultrasound (TRUS) images. For this purpose, manual segmentation is a tedious and time consuming procedure.MethodsWe introduce a new method for the segmentation of the prostate in transrectal ultrasound images, using a reinforcement learning scheme. This algorithm is used to find the appropriate local values for sub-images and to extract the prostate. It contains an offline stage, where the reinforcement learning agent uses some images and manually segmented versions of these images to learn from. The reinforcement agent is provided with reward/punishment, determined objectively to explore/exploit the solution space. After this stage, the agent has acquired knowledge stored in the Q-matrix. The agent can then use this knowledge for new input images to extract a coarse version of the prostate.ResultsWe have carried out experiments to segment TRUS images. The results demonstrate the potential of this approach in the field of medical image segmentation.ConclusionBy using the proposed method, we can find the appropriate local values and segment the prostate. This approach can be used for segmentation tasks containing one object of interest. To improve this prototype, more investigations are needed.

Filter fusion for image enhancement using reinforcement learning

A new approach to image enhancement based on fusion of a number of filters using a reinforcement learning scheme is presented. In most applications the result of applying a single filter is usually unsatisfactory. Appropriate fusion of the results of several different filters, such as median, local average, sharpening, and Wiener filters, can resolve this difficulty. Many different techniques already exist in literatures. In this work, a reinforcement-learning agent will be proposed. During learning, the agent takes some actions (i.e., different weights for filters) to change its environment (the image quality). Reinforcement is provided by a scalar evaluation determined subjectively by the user. The approach has several advantages. The user interaction eliminates the need for objective image quality measures. No formal user model is required. Finally, no training data is necessary. The paper describes the implementation and evaluation of a global reinforced adjustment of the weights of the different filters.

A Reinforcement Learning Framework for Medical Image Segmentation

This paper introduces a new method to medical image segmentation using a reinforcement learning scheme. We use this novel idea as an effective way to optimally find the appropriate local thresholding and structuring element values and segment the prostate in ultrasound images. Reinforcement learning agent uses an ultrasound image and its manually segmented version and takes some actions (i.e., different thresholding and structuring element values) to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal determined objectively. The agent uses these objective reward/punishment to explore/exploit the solution space. The values obtained using this way can be used as valuable knowledge to fill a Q-matrix. The reinforcement learning agent can use this knowledge for similar ultrasound images as well. The results demonstrate high potential for applying reinforcement learning in the field of medical image segmentation.

Segmentation of prostate boundaries using regional contrast enhancement

In this paper a novel method for automatic prostate segmentation in transrectal ultrasound images is presented. Morphological grey level transformations are first used to generate an image with enough bright intensity around the prostate. This image is then thresholded to produce a binary image. Then by finding and using a point as the inside point for the prostate, a Kalman estimator is used to isolate the prostate boundary from any irrelevant parts and produce a roughly segmented version (as coarse estimation). Consequently, a fuzzy inference system describing regional and gray level information is employed to enhance the contrast of the prostate with respect to the background. Using strong edges obtained from this enhanced image and information from pixels gradients and also the characteristics in the vicinity of the coarse estimation, the final boundary is extracted. A number of experiments are conducted to validate this method.

Contrast enhancement of mammography images using a fuzzy approach

In this paper, a fuzzy operator for contrast enhancement of mammography images is presented. This intensity modification method is effective and fast in computing. In this approach, the selection of appropriate parameters for the required transformations is performed based on the specific image characteristics. Images are first transformed using a fuzzification function. We then apply an algorithm for intensity adaptation based on involutive fuzzy complements and measures of fuzziness. Regarding to the amount of ambiguity the proposed technique detects the suitable form of the modification in the set of involutive membership function. Finally, the defuzzification procedure is performed to transform the modified image back to the spatial domain. The experimental results show that this method can enhance the region of interest in mammography images that is useful for breast cancer diagnosis.

A reinforcement agent for object segmentation in ultrasound images

The principal contribution of this work is to design a general framework for an intelligent system to extract one object of interest from ultrasound images. This system is based on reinforcement learning. The input image is divided into several sub-images, and the proposed system finds the appropriate local values for each of them so that it can extract the object of interest. The agent uses some images and their ground-truth (manually segmented) version to learn from. A reward function is employed to measure the similarities between the output and the manually segmented images, and to provide feedback to the agent. The information obtained can be used as valuable knowledge stored in the Q-matrix. The agent can then use this knowledge for new input images. The experimental results for prostate segmentation in trans-rectal ultrasound images show high potential of this approach in the field of ultrasound image segmentation.

Increasing Object Recognition Rate using Reinforced Segmentation

In this paper a new approach to object extraction and recognition based on reinforcement learning is presented. We use this novel idea as a method to optimally segment the image and increase the recognition rate. The success rate is compared with a classical approach. Preliminary results demonstrate increase in recognition rate.