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Investigation on ROI size and location to classify mammograms
摘要： Breast cancer is the major cause of death among women and early detection can lead to a longer survival. Computer Aided Diagnosis (CAD) system helps radiologists in the accurate detection of breast cancer. In medical images a Region of Interest (ROI) is a portion of image which carries the important information related to the diagnosis and it forms the basis for applying shape and texture techniques for cancer detection. Several ROI sizes and locations have been proposed for computer aided diagnosis systems. In the present work various ROI sizes have been used to determine the appropriate ROI size to classify fatty and dense mammograms. Two types of mammograms i.e. fatty and dense are used from the MIAS database. Various texture features have been determined from each ROI size for the analysis of texture characteristics. Fisher discriminant ratio is used to select the most relevant features for classification. Finally linear SVM is used for the purpose of classification. Highest classification accuracy of 96.1% was achieved for ROI size 200×200 pixels.
关键词： classification,breast cancer,digital mammograms,breast tissue,ROI,feature selection
更新于2025-09-23 15:22:29
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An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm
摘要： Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.
关键词： DWT,Medical image,non-ROI,modified region growing,DCT,region of interest,merging-based Huffman encoding,SPIHT
更新于2025-09-23 15:22:29
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Deep-learning based surface region selection for deep inspiration breath hold (DIBH) monitoring in left breast cancer radiotherapy
摘要： Deep inspiration breath hold (DIBH) with surface supervising is a common technique for cardiac dose reduction in left breast cancer radiotherapy. Surface supervision accuracy relies on the characteristics of surface region. In this study, a convolutional neural network (CNN) based automatic region-of-interest (ROI) selection method was proposed to select an optimal surface ROI for DIBH surface monitoring. The curvature entropy and the normal of each vertex on the breast cancer patient surface were calculated and formed as representative maps for ROI selection learning. 900 ROIs were randomly extracted from each patient’s surface representative map, and the corresponding rigid ROI registration errors (RE) were calculated. The VGG-16 (a 16-layer network structure developed by Visual Geometry Group(VGG) from University of Oxford) pre-trained on a large natural image database ImageNet were fine-tuned using 27 thousand extracted ROIs and the corresponding RE from thirty patients. The RE prediction accuracy of the trained model was validated on additional ten patients. Satisfactory RE predictive accuracies were achieved with the root mean square error (RMSE)/mean absolute error (MAE) smaller than 1mm/0.7mm in translations and 0.45°/0.35° in rotations, respectively. The REs of the model selected ROIs on ten testing cases is close to the minimal predicted RE with mean RE differences <1mm and <0.5° for translation and rotation, respectively. The proposed RE predictive model can be utilized for selecting a quasi-optimal ROI in left breast cancer DIBH radiotherapy (DIBH-RT).
关键词： DIBH,ROI selection,transfer learning,motion monitoring
更新于2025-09-23 15:21:21
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Advances in Concentrating Solar Thermal Research and Technology || Advanced control strategies to maximize ROI and the value of the concentrating solar thermal (CST) plant to the grid
摘要： There are two main drawbacks to concentrating solar thermal energy systems: (1) the resulting energy costs are not yet competitive and (2) solar energy is not always available when needed. Considerable research efforts are being devoted to techniques that may help to overcome these drawbacks; control is one of those techniques [1]. One of the main challenges identified by the US National Academy of Engineering is to make solar energy economical [2]. This issue can be addressed by reducing investment and operating costs and increasing the solar plant performance [3]. Advanced control techniques can help to reduce operating costs and increase the solar plant performance. This chapter describes two examples of how advanced control techniques can help to optimize operation of solar plants and, in consequence, maximize the return of investment (ROI).
关键词： advanced control strategies,solar tower plants,solar trough plants,concentrating solar thermal,ROI
更新于2025-09-23 15:21:01
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The OCS method of seeding point detection using visible vision for large-diameter sapphire single crystal growth via the Kyropoulos method
摘要： The seeding process is vital in the preparation of large-diameter sapphire single crystal. It is the key to detect the seeding point during the seeding process. The OCS method is proposed in the paper to detect the seeding point. The OCS method improves the detection method of spoke pattern center propsed by Churl Min Kim, based on this, and the convergence model of spoke pattern center is fitted, sothat the real seeding point is detected. In the experiment, the OCS method is verified by comparing with the traditional manual seeding method (operated by skilled seeding technologists) and the method proposed by Churl Min Kim. The OCS method has the same effect as the traditional artificial seeding method and can reduce the number of attempts in a single seeding experiment. Compared with the method propsed by Churl Min Kim, the OCS method can meet the needs of actual industrial production in terms of the number of successful seeding, the number of seeding attempts and the average seeding time.
关键词： Skeletonizing,Seeding point detection,Convergence model fitting,Observation Convergence Seeding (OCS),Coner detection,ROI locking
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE Conference on Control Technology and Applications (CCTA) - Hong Kong, China (2019.8.19-2019.8.21)] 2019 IEEE Conference on Control Technology and Applications (CCTA) - Novel Micro Scanning with Integrated Atomic Force Microscope and Confocal Laser Scanning Microscope
摘要： Integrated atomic force microscope (AFM) and confocal laser scanning microscope (CLSM) can quickly obtain the three-dimensional (3-D) surface of the sample in large scanning range and recover the region of interesting (ROI) in nanoscale resolution. However, the traditional cooperative algorithm for integrated microscopes occupies too much scanning time. In this work, we develop a novel cooperative algorithm for the integrated microscopes to reduce scanning time of AFM and achieve higher scanning speed. First, the calibration of the microscopes will be implemented. Next, CLSM starts a large range scan first and then define the region of interesting (ROI) by edge detection. And then, the scan regions of the AFM are arranged based on the ROI and adaptive scanning region method is proposed to reduce the scanning time. Furthermore, variable speed scanning based on the height information obtained from CLSM image is applied to increase the AFM scanning speed. Finally, the scanning images obtained from AFM and CLSM are merged together. A series of experimental results show that proposed cooperative algorithm can save approximately 69.2% of scanning time compared with that obtained by traditional cooperative algorithm.
关键词： Atomic force microscope (AFM),adaptive scanning range,confocal laser scanning microscope (CLSM),regions of interest (ROI),variable speed scanning
更新于2025-09-16 10:30:52
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Return of Interest Planning for Photovoltaics Connected with Energy Storage System by Considering Maximum Power Demand
摘要： In this study, a general building of medium size with an Energy Storage Systems (ESS)-connected Photovoltaic (PV) system (energy storage system that is connected to a photovoltaic system) was chosen to develop a tool for a better economic evaluation of its installation and use. The newly obtained results, from the revised economic evaluation algorithm that was proposed in this study, showed the e?ective return of investment period (ROI) would be 8.62 to 12.77 years. The ratio of maximum power demand to contract demand and the falling cost of PVs and ESS was the factors that could a?ect the ROI. While using the cost scenario of PVs and ESS from 2019 to 2024, as estimated by the experts, the ROI was signi?cantly improved. The ROI was estimated to be between 4.26 to 8.56 years by the year 2024 when the cost scenario was considered. However, this result is obtained by controlling the ratio of maximum power demand to contract demand. Continued favorable government policies concerning renewable energy would be crucial in expanding the supply and investment in renewable energy resources, until the required ROI is attained.
关键词： economic feasibility,maximum power demand per contract demand,ROI,building energy management system (BEMS),ESS connected PV system
更新于2025-09-16 10:30:52
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[IEEE 2018 International CET Conference on Control, Communication, and Computing (IC4) - Thiruvananthapuram, India (2018.7.5-2018.7.7)] 2018 International CET Conference on Control, Communication, and Computing (IC4) - Reversible and Secure False Color Based Image Privacy Protection with Color Palette Generation
摘要： In many areas, visual privacy needs the protection, especially in video surveillance. Video surveillance is considered as an important solution for the security and safety issues in public places, since it monitors behaviour, activities, or other changing information. Its usage is increased in today’s world which affects individual’s privacy. There comes the need of visual privacy protection in video surveillance. Local servers can not store and analyze this large amount of data from the surveillance camera. So usage of cloud servers can solve this problem. But there is a chance to acquire these data by unauthorized parties. Different schemes for data hiding are present, which reversibly encrypts this data. But such systems need to do the detection of sensitive regions either by using a computer vision module or manually which increases complexity and reduces reliability. So two fully reversible privacy protection schemes for images using false coloring can be used. First scheme is more generic i.e., it is ?exible with other privacy protection schemes and the next scheme is fully based on false colors. These schemes are independent of region-of-interest (ROI) detection and can be applied to the whole image. So the user can escape from the complex ROI detection. A color palette generation method is also added with these schemes which increases the security.
关键词： k-means clustering,scrambling,pixelation,ROI,blurring,privacy protection,false coloring
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Region of Interest Detection based on Local Entropy Feature for Disaster Victim Detection System
摘要： Region of interest (ROI) detection plays an important role in object detection. It needs to be accurate and fast in some applications like real time disaster victim detection systems. ROI can reduce time and search space in detecting objects. In this paper visual saliency map is used for ROI detection. In most literature, most of ROI detection models only concentrate on reducing false positive (detecting wrong objects as intended ones) rate rather than false negative (missing intended object). In disaster victim detection, missing disaster victims is more important than detecting other objects like victim. So, the proposed method also focuses on reducing false negative error rate in object detection. In the proposed system, local entropy feature is added in Graph Based Visual Saliency (GBVS) map in addition to colour, orientation and shape feature maps.
关键词： GBVS,local entropy feature map,ROI detection,object detection,false negative
更新于2025-09-04 15:30:14