scholarly journals Improved diagnosis and navigation for CT colonography

2021 ◽  
Author(s):  
Jinjie Ming
Keyword(s):  
Ct Colonography ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Polyp Detection ◽  
Data Set ◽  
Colon Wall ◽  
Texture Information ◽  
Ct Data ◽  
New Feature ◽  
Shape And Size

This project describes the development of an automatic segmentation method and a novel navigation system that detect polyps using advanced image processing and computer graphics tecniques. The colon wall segmentation method from the CT data set of abdomen is achieved by combining the contouring model - level set method and the minima detection using mathematical morphology theory. Polyp detection is attained by analyzing surface curvature and texture information along on the colon wall. Adding texture analysis provides a new feature for improving currently existing methods. As such, polyp candidates are examined not only by their shape and size but also by their texture appearance.

scholarly journals Improved diagnosis and navigation for CT colonography

2021 ◽  
Author(s):  
Jinjie Ming
Keyword(s):  
Ct Colonography ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Polyp Detection ◽  
Data Set ◽  
Colon Wall ◽  
Texture Information ◽  
Ct Data ◽  
New Feature ◽  
Shape And Size

This project describes the development of an automatic segmentation method and a novel navigation system that detect polyps using advanced image processing and computer graphics tecniques. The colon wall segmentation method from the CT data set of abdomen is achieved by combining the contouring model - level set method and the minima detection using mathematical morphology theory. Polyp detection is attained by analyzing surface curvature and texture information along on the colon wall. Adding texture analysis provides a new feature for improving currently existing methods. As such, polyp candidates are examined not only by their shape and size but also by their texture appearance.

scholarly journals Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gurman Gill ◽  
Reinhard R. Beichel
Keyword(s):  
Image Data ◽  
Active Shape Model ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Set ◽  
Shape Model ◽  
Lung Segmentation ◽  
4D Ct ◽  
Active Shape ◽  
Ct Data

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of0.9773±0.0254, which was statistically significantly better (pvalue≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

Automatic Fish Segmentation and Recognition in Taiwan Fish Market using Deep Learning Techniques

2021 ◽  
Author(s):  
Ching-Han Chen ◽  
Lu-Hsuan Chen ◽  
Ching-Yi Chen
Keyword(s):  
Fish Species ◽  
Automatic Segmentation ◽  
Recognition System ◽  
Input Image ◽  
Classification Model ◽  
Light Sources ◽  
Segmentation Method ◽  
Data Set ◽  
Fish Market ◽  
Learning Techniques

Taiwan fish markets sell a wide variety of fish, and laypeople may have difficulty recognizing the fish species. The identification of fish species is still mostly based on illustrated handbooks, which is time-consuming when users lack experience. Automatic segmentation and recognition of fish images are important for the field of oceanography. However, in fish markets, the instability of light sources and changes in illumination influence the brightness and colors of fish. Moreover, fish markets often arrange fish together and cover them with ice to keep them fresh, thus increasing the difficulty of automatic fish recognition. This study presents a fish recognition system that combines a state-of-art instance segmentation method along with ResNet-based classification. An input image is first passed through the fish segmentation model, which crops the image into several images containing specific objects with a plain black background. Then the cropped images are assigned to a class by the fish classification model, which returns the predicted label of each image. A database of real fish images was collected from a fish market to verify the system. The experimental results revealed that the system achieved 85% Top-1 accuracy and 95% Top-5 accuracy on the test data set.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

Automatic Segmentation Method of Bone Conduction Sound for Eating Activity Detailed Detection

2021 ◽  
Author(s):  
Haruka Kamachi ◽  
Takumi Kondo ◽  
Tahera Hossain ◽  
Anna Yokokubo ◽  
Guillaume Lopez
Keyword(s):  
Automatic Segmentation ◽  
Bone Conduction ◽  
Segmentation Method

scholarly journals An automatic segmentation method for heart sounds

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Qingshu Liu ◽  
Xiaomei Wu ◽  
Xiaojing Ma
Keyword(s):  
Automatic Segmentation ◽  
Heart Sounds ◽  
Segmentation Method

EDIBLE BIRD NEST SHAPE INSPECTION USING FOURIER DESCRIPTOR (FD) AND FARTHEST FOURIER POINT SIGNATURE (FFPS) METHOD

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
F. S. A. Sa’ad ◽  
M. F. Ibrahim ◽  
A. Y. M. Shakaff ◽  
A. Zakaria
Keyword(s):  
Hybrid Method ◽  
Vision System ◽  
Image Data ◽  
Fourier Descriptor ◽  
Data Set ◽  
Charge Coupled Device ◽  
Bird Nest ◽  
Wilks Lambda ◽  
Shape Inspection ◽  
Shape And Size

Swiftlets are birds contained within the four genera Aerodramus, Hydrochous, Schoutedenapus and Collocalia. To date, the bird nest grading is based on weight, shape and size. Current inspection and grading for raw, edible bird nest were performed visually by expert panels. This conventional method is relying more on human judgments and often biased. A novel hybrid method from Fourier Descriptor (FD) method and Farthest Fourier Point Signature (FFPS) was developed using Charge Coupled Device (CCD) image data to grade bird nest by its shape and size. From the result, the hybrid method was able to differentiate different shape such as super AAA, super and corner grade depending on the Swiftlet species and geographical origin. The Wilks' lambda analysis was invoked to transform and compress the data set comprising of a large number of interconnected variables to a reduced set of varieties. Overall, the vision system was able to correctly classify 92.6 % of the super AAA, super and Corner shaped grades using the combined FD and FFPS features.

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