Performance evaluation of simple linear iterative clustering algorithm on medical image processing

Cong, Jinyu; Wei, Benzheng; Yin, Yilong; Xi, Xiaoming; Zheng, Yuanjie

doi:10.3233/bme-141145

Cited by 9 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the good boundary adherence of the SLIC algorithm on natural images, in which the object and background frequently manifest different texture appearances just as they do in ultrasound images, it is reasonable to expect that SLIC has a good boundary adherence in ultrasound images. Another study reported in [ 26 ] also showed that the computing speed and boundary performance of SLIC surpass that of two other superpixel algorithms according to the experiment results based on medical images, including ultrasound images. For the fastest segmentation speed and reasonable adherence, SLIC was adopted as the superpixel generation algorithm here.…”

Section: Methodsmentioning

confidence: 91%

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy

Zhang

Yang

et al. 2015

PLoS ONE

View full text Add to dashboard Cite

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC) platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning.

show abstract

Section: Methodsmentioning

confidence: 91%

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy

Zhang

Yang

et al. 2015

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In this study, we focused on the texture of the chest X-ray images and incorporated the SLIC algorithm. The SLIC is a typical algorithm of superpixel generation and is one of the most prominent superpixel segmentation algorithms [14,15]. A superpixel can be defined as a group of pixels that share similar properties.…”

Section: Methodsmentioning

confidence: 99%

“…The SLIC is an efficient superpixel generation algorithm based on k-means clustering [14,15]. It consists of the following three main stages:…”

Section: Brief Overview Of Simple Linear Iterative Clustering (Slic) Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images

Matsuyama¹

2021

JBiSE

View full text Add to dashboard Cite

Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) systems for chest radiography. However, if the chest X-ray images themselves are used as training data for the AI-CAD system, the system might learn the irrelevant image-based information resulting in the decrease of system's performance. In this study, we propose a lung region segmentation method that can automatically remove the shoulder and scapula regions, mediastinum, and diaphragm regions in advance from various chest X-ray images to be used as learning data. The proposed method consists of three main steps. First, employ the simple linear iterative clustering algorithm, the lazy snapping technique and local entropy filter to generate an entropy map. Second, apply morphological operations to the entropy map to obtain a lung mask. Third, perform automated segmentation of the lung field using the obtained mask. A total of 30 images were used for the experiments. In order to verify the effectiveness of the proposed method, two other texture maps, namely, the maps created from the standard deviation filtering and the range filtering, were used for comparison. As a result, the proposed method using the entropy map was able to appropriately remove the unnecessary regions. In addition, this method was able to remove the markers present in the image, but the other two methods could not. The experimental results have revealed that our proposed method is a highly generalizable and useful algorithm. We believe that this method might act an important role to enhance the performance of AI-CAD systems for chest X-ray images.

show abstract

“…The number of pixels is very large, and there are problems in efficiency. Therefore, this paper uses SLIC [38] (Simple Linear Iterative Clustering, SLIC) to perform super pixel segmentation on the original image of medical motion, using the obtained super pixel as the basic unit. The use of super pixels can avoid the influence of individual noise pixels on the classification result and improve the anti-interference ability.…”

Section: Medical Exercise Rehabilitation Image Segmentation Algormentioning

confidence: 99%

Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Ding

Ren

2019

IEEE Access

View full text Add to dashboard Cite

Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

show abstract

Performance evaluation of simple linear iterative clustering algorithm on medical image processing

Cited by 9 publications

References 12 publications

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy

A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images

Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Contact Info

Product

Resources

About