Interactive Volume Exploration for Feature Detection and Quantification in Industrial CT Data

2008 ◽  
Vol 14 (6) ◽  
pp. 1507-1514 ◽  
Author(s):  
M. Hadwiger ◽  
F. Laura ◽  
C. Rezk-Salama ◽  
T. Hollt ◽  
G. Geier ◽  
...  
Keyword(s):  
Feature Detection ◽  
Industrial Ct ◽  
Ct Data ◽  
Volume Exploration ◽  
Detection And Quantification

scholarly journals Image Processing of Conventional Computer Tomography Images for Segmentation of the Human Cochlea

2021 ◽  
Author(s):  
Jenny Stritzel ◽  
Dominik Wolff ◽  
Klaus-Hendrik Wolf ◽  
Tobias Weller ◽  
Thomas Lenarz ◽  
...  
Keyword(s):  
Feature Detection ◽  
Research Work ◽  
Surrounding Tissue ◽  
Prediction Tool ◽  
Hearing Outcome ◽  
Human Cochlea ◽  
Preoperative Ct ◽  
Ct Data ◽  
Conventional Ct ◽  
Conventional Computer

Against the background of increasing numbers of indications for Cochlea implants (CIs), there is an increasing need for a CI outcome prediction tool to assist the process of deciding on the best possible treatment solution for each individual patient prior to intervention. The hearing outcome depends on several features in cochlear structure, the influence of which is not entirely known as yet. In preparation for surgical planning a preoperative CT scan is recorded. The overall goal is the feature extraction and prediction of the hearing outcome only based on this conventional CT data. Therefore, the aim of our research work for this paper is the preprocessing of the conventional CT data and a following segmentation of the human cochlea. The great challenge is the very small size of the cochlea in combination with a fairly bad resolution. For a better distinction between cochlea and surrounding tissue, the data has to be rotated in a way the typical cochlea shape is observable. Afterwards, a segmentation can be performed which enables a feature detection. We can show the effectiveness of our method compared to results in literature which were based on CT data with a much higher resolution. A further study with a much larger amount of data is planned.

scholarly journals Three-dimensional segmentation of computed tomography data using Drishti Paint : new tools and developments

2020 ◽  
Vol 7 (12) ◽  
pp. 201033
Author(s):  
Yuzhi Hu ◽  
Ajay Limaye ◽  
Jing Lu
Keyword(s):  
Computed Tomography ◽  
Open Source ◽  
Three Dimensional ◽  
Volume Data ◽  
Digital Reconstruction ◽  
Ct Data ◽  
Scan Data ◽  
Volume Exploration ◽  
Three Dimensional Segmentation ◽  
Tomography Data

Computed tomography (CT) has become very widely used in scientific and medical research and industry for its non-destructive and high-resolution means of detecting internal structure. Three-dimensional segmentation of computed tomography data sheds light on internal features of target objects. Three-dimensional segmentation of CT data is supported by various well-established software programs, but the powerful functionalities and capabilities of open-source software have not been fully revealed. Here, we present a new release of the open-source volume exploration, rendering and three-dimensional segmentation software, Drishti v. 2.7. We introduce a new tool for thresholding volume data (i.e. gradient thresholding) and a protocol for performing three-dimensional segmentation using the 3D Freeform Painter tool. These new tools and workflow enable more accurate and precise digital reconstruction, three-dimensional modelling and three-dimensional printing results. We use scan data of a fossil fish as a case study, but our procedure is widely applicable in biological, medical and industrial research.

Feature Detection Requires Spatial Attention

2007 ◽  
Author(s):  
Jan Theeuwes ◽  
Erik van der Burg ◽  
Artem V. Belopolsky
Keyword(s):  
Spatial Attention ◽  
Feature Detection

Blinded re-analysis of CT-data with HeartNavigator®: Improved TAVI planning versus TEE and CT alone

2013 ◽  
Vol 61 (S 01) ◽  
Author(s):  
C Schmidtke ◽  
D Richardt ◽  
M Strauch ◽  
J Barkhausen ◽  
HH Sievers ◽  
...  
Keyword(s):  
Ct Data

Effect of CT-based attenuation correction on uptake ratios in skeletal SPECT

2007 ◽  
Vol 46 (01) ◽  
pp. 38-42 ◽  
Author(s):  
V. Schulz ◽  
I. Nickel ◽  
A. Nömayr ◽  
A. H. Vija ◽  
C. Hocke ◽  
...  
Keyword(s):  
Vertebral Body ◽  
Facet Joint ◽  
One Dimension ◽  
Patient Specific ◽  
Data Sets ◽  
Facet Joints ◽  
Lumbar Vertebral Body ◽  
Ct System ◽  
Ct Data ◽  
Attenuation Corrected

SummaryThe aim of this study was to determine the clinical relevance of compensating SPECT data for patient specific attenuation by the use of CT data simultaneously acquired with SPECT/CT when analyzing the skeletal uptake of polyphosphonates (DPD). Furthermore, the influence of misregistration between SPECT and CT data on uptake ratios was investigated. Methods: Thirty-six data sets from bone SPECTs performed on a hybrid SPECT/CT system were retrospectively analyzed. Using regions of interest (ROIs), raw counts were determined in the fifth lumbar vertebral body, its facet joints, both anterior iliacal spinae, and of the whole transversal slice. ROI measurements were performed in uncorrected (NAC) and attenuation-corrected (AC) images. Furthermore, the ROI measurements were also performed in AC scans in which SPECT and CT images had been misaligned by 1 cm in one dimension beforehand (ACX, ACY, ACZ). Results: After AC, DPD uptake ratios differed significantly from the NAC values in all regions studied ranging from 32% for the left facet joint to 39% for the vertebral body. AC using misaligned pairs of patient data sets led to a significant change of whole-slice uptake ratios whose differences ranged from 3,5 to 25%. For ACX, the average left-to-right ratio of the facet joints was by 8% and for the superior iliacal spines by 31% lower than the values determined for the matched images (p <0.05). Conclusions: AC significantly affects DPD uptake ratios. Furthermore, misalignment between SPECT and CT may introduce significant errors in quantification, potentially also affecting leftto- right ratios. Therefore, at clinical evaluation of attenuation- corrected scans special attention should be given to possible misalignments between SPECT and CT.

Preoperative assessment of treatment planning on minimization of micro-movement during healing period of immediate-loaded implants using X-ray CT data-based simulation

2010 ◽  
pp. 110220121911010
Author(s):  
KATSUHIKO KIMURA ◽  
Yasumasa Fukase ◽  
Michiko Makino ◽  
Chihiro Masaki ◽  
Tetsuji Nakamoto ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Preoperative Assessment ◽  
X Ray ◽  
Healing Period ◽  
Ct Data

Computerized Modeling Based on Spiral CT Data for Noninvasive Determination of Aortic Stent-Graft Length

2002 ◽  
Vol 9 (4) ◽  
pp. 520-528 ◽  
Author(s):  
Albert Rott ◽  
Thomas Boehm ◽  
Joachim Söldner ◽  
Jürgen R. Reichenbach ◽  
Jürgen Heyne ◽  
...  
Keyword(s):  
Stent Graft ◽  
Spiral Ct ◽  
Aortic Stent Graft ◽  
Ct Data ◽  
Graft Length

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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