Design and evaluation of a new automated method for the segmentation and characterization of masses on ultrasound images

2008 ◽  
Author(s):  
Jing Cui ◽  
Berkman Sahiner ◽  
Heang-Ping Chan ◽  
Alexis Nees ◽  
Chintana Paramagul ◽  
...  
Keyword(s):  
Ultrasound Images ◽  
Automated Method

scholarly journals A new automated method for the segmentation and characterization of breast masses on ultrasound images

2009 ◽  
Vol 36 (5) ◽  
pp. 1553-1565 ◽  
Author(s):  
Jing Cui ◽  
Berkman Sahiner ◽  
Heang-Ping Chan ◽  
Alexis Nees ◽  
Chintana Paramagul ◽  
...  
Keyword(s):  
Ultrasound Images ◽  
Breast Masses ◽  
Automated Method

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

Comparison of microscopy to a semi-automated method (FlowCAM®) for characterization of individual-, population-, and community-level measurements of zooplankton

Hydrobiologia ◽  
2019 ◽  
Vol 838 (1) ◽  
pp. 99-110 ◽  
Author(s):  
Thomas M. Detmer ◽  
Kyle J. Broadway ◽  
Cal G. Potter ◽  
Scott F. Collins ◽  
Joseph J. Parkos ◽  
...  
Keyword(s):  
Community Level ◽  
Automated Method

Ultrasound Imaging Characterization of Soft Tissue Dynamics of the Seated Human Body

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Daisuke Yamada ◽  
Alperen Değirmenci ◽  
Robert D. Howe
Keyword(s):  
Ultrasound Imaging ◽  
Human Body ◽  
Human Subjects ◽  
Excitation Amplitude ◽  
Whole Body ◽  
Ultrasound Images ◽  
Body Dynamics ◽  
Excitation Pattern

Abstract To characterize the dynamics of internal soft organs and external anatomical structures, this paper presents a system that combines medical ultrasound imaging with an optical tracker and a vertical exciter that imparts whole-body vibrations on seated subjects. The spatial and temporal accuracy of the system was validated using a phantom with calibrated internal structures, resulting in 0.224 mm maximum root-mean-square (r.m.s.) position error and 13 ms maximum synchronization error between sensors. In addition to the dynamics of the head and sternum, stomach dynamics were characterized by extracting the centroid of the stomach from the ultrasound images. The system was used to characterize the subject-specific body dynamics as well as the intrasubject variabilities caused by excitation pattern (frequency up-sweep, down-sweep, and white noise, 1–10 Hz), excitation amplitude (1 and 2 m/s2 r.m.s.), seat compliance (rigid and soft), and stomach filling (empty and 500 mL water). Human subjects experiments (n = 3) yielded preliminary results for the frequency response of the head, sternum, and stomach. The method presented here provides the first detailed in vivo characterization of internal and external human body dynamics. Tissue dynamics characterized by the system can inform design of vehicle structures and adaptive control of seat and suspension systems, as well as validate finite element models for predicting passenger comfort in the early stages of vehicle design.

Automated location of thyroid nodules in ultrasound images with improved YOLOV3 network

2020 ◽  
pp. 1-16
Author(s):  
Ling Zhang ◽  
Yan Zhuang ◽  
Zhan Hua ◽  
Lin Han ◽  
Cheng Li ◽  
...  
Keyword(s):  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ultrasound Images ◽  
Semantic Features ◽  
Low Contrast ◽  
Automated Method

BACKGROUND: Thyroid ultrasonography is widely used to diagnose thyroid nodules in clinics. Automatic localization of nodules can promote the development of intelligent thyroid diagnosis and reduce workload of radiologists. However, besides the ultrasound image has low contrast and high noise, the thyroid nodules are diverse in shape and vary greatly in size. Thus, thyroid nodule detection in ultrasound images is still a challenging task. OBJECTIVE: This study proposes an automatic detection algorithm to locate nodules in B ultrasound images and Doppler ultrasound images. This method can be used to screen thyroid nodules and provide a basis for subsequent automatic segmentation and intelligent diagnosis. METHODS: We develop and optimize an improved YOLOV3 model for detecting thyroid nodules in ultrasound images with B-mode and Doppler mode. Improvements include (1) using the high-resolution network (HRNet) as the basic network for gradually extracting high-level semantic features to reduce the missed detection and misdetection, (2) optimizing the loss function for single target detection like nodules, and (3) obtaining the anchor boxes by clustering the candidate frames of real nodules in the dataset. RESULTS: The experimental results of applying to 8000 clinical ultrasound images show that the new method developed and tested in this study can effectively detect thyroid nodules. The method achieves 94.53% mean precision and 95.00% mean recall. CONCLUTIONS: The study demonstrates a new automated method that enables to achieve high detection accuracy and effectively locate thyroid nodules in various ultrasound images without any user interaction, which indicates its potential clinical application value for the thyroid nodule screening.

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