Combining Image Reconstruction and Image Analysis with an Application to Two-Dimensional Tomography

We review computerized cancer cell image analysis and visualization research over the past 30 years. Image acquisition, feature extraction, classification, and visualization from two-dimensional to three-dimensional image algorithms are introduced with case studies of bladder, prostate, breast, and renal carcinomas.

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Automated three-dimensional measurements of version, inclination, and subluxation

Shoulder & Elbow ◽

10.1177/1758573218825480 ◽

2019 ◽

Vol 12 (1) ◽

pp. 31-37

Author(s):

Dave R Shukla ◽

Richard J McLaughlin ◽

Julia Lee ◽

Ngoc Tram V Nguyen ◽

Joaquin Sanchez-Sotelo

Keyword(s):

Computed Tomography ◽

Image Analysis ◽

Three Dimensional ◽

Automated Image Analysis ◽

Two Dimensional ◽

Analysis Software ◽

Glenoid Version ◽

Image Analysis Software ◽

Planning Software ◽

Dimensional Measurements

Background Preoperative planning software has been developed to measure glenoid version, glenoid inclination, and humeral head subluxation on computed tomography (CT) for shoulder arthroplasty. However, most studies analyzing the effect of glenoid positioning on outcome were done prior to the introduction of planning software. Thus, measurements obtained from the software can only be extrapolated to predict failure provided they are similar to classic measurements. The purpose of this study was to compare measurements obtained using classic manual measuring techniques and measurements generated from automated image analysis software. Methods Ninety-five two-dimensional computed tomography scans of shoulders with primary glenohumeral osteoarthritis were measured for version according to Friedman method, inclination according to Maurer method, and subluxation according to Walch method. DICOM files were loaded into an image analysis software (Blueprint, Wright Medical) and the output was compared with values obtained manually using a paired sample t-test. Results Average manual measurements included 13.8° version, 13.2° inclination, and 56.2% subluxation. Average image analysis software values included 17.4° version (3.5° difference, p < 0.0001), 9.2° inclination (3.9° difference, p < 0.001), and 74.2% for subluxation (18% difference, p < 0.0001). Conclusions Glenoid version and inclination values from the software and manual measurement on two-dimensional computed tomography were relatively similar, within approximately 4°. However, subluxation measurements differed by approximately 20%.

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Three-Dimensional Image Analysis of Aggregate Particles from Orthogonal Projections

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198196152600112 ◽

1996 ◽

Vol 1526 (1) ◽

pp. 98-103 ◽

Cited By ~ 28

Author(s):

C.-Y. Kuo ◽

J.D. Frost ◽

J.S. Lai ◽

L.B. Wang

Keyword(s):

Image Analysis ◽

Three Dimensional ◽

Two Dimensional ◽

Uniform Size ◽

Standard Size ◽

Orthogonal Projections ◽

Projected Image ◽

Dimensional Image ◽

Area And Perimeter ◽

Aggregate Particles

Digital image analysis provides the capability for rapid measurement of particle characteristics. When an image is captured and digitized, numerous measurements can be made in near real time for each particle. Usually, image analysis techniques treat particles as two-dimensional objects since only the two-dimensional projection of the particles is captured. In this study, three-dimensional analysis of aggregate particles that was performed by attaching aggregates in sample trays with two perpendicular faces is described. After the initial projected image of the aggregates is captured and measured, the sample trays are rotated 90 degrees so that the aggregates are now perpendicular to their original orientation and the dimensions of the aggregates in the new projected image are captured and measured. The long, intermediate, and short particle dimensions ( dL, dI, and dS, respectively) provide direct measures of the flatness and elongation of the particles. Some other shape indexes can also be derived from the measurements of area and perimeter length. The proposed image analysis method was verified by comparing the results obtained with manual measurements of particle dimensions for uniform size [passing 12.7 mm (1/2 in.) sieve and retained on 9.5 mm (3/8 in.) sieve] aggregates. Three-dimensional image analysis was also performed on five aggregates of standard size No. 89 from different sources, and the results are summarized herein. The proposed method is expected to improve field quality control of aggregates used in hot mix asphalt.

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Analytic time-of-flight positron emission tomography reconstruction: two-dimensional case

Visual Computing for Industry Biomedicine and Art ◽

10.1186/s42492-019-0035-4 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 2

Author(s):

Gengsheng L. Zeng ◽

Ya Li ◽

Qiu Huang

Keyword(s):

Positron Emission Tomography ◽

Image Reconstruction ◽

Iterative Algorithm ◽

Emission Tomography ◽

Two Dimensional ◽

Profile Function ◽

Positron Emission ◽

Pet Image Reconstruction ◽

2D Filter ◽

Tof Pet

AbstractIn a positron emission tomography (PET) scanner, the time-of-flight (TOF) information gives us rough event position along the line-of-response (LOR). Using the TOF information for PET image reconstruction is able to reduce image noise. The state-of-the-art TOF PET image reconstruction uses iterative algorithms. Analytical image reconstruction algorithm exits for TOF PET which emulates the iterative Landweber algorithm. This paper introduces such an algorithm, focusing on two-dimensional (2D) reconstruction. The proposed algorithm is in the form of backprojection filtering, in which the backprojection is performed first, and then a 2D filter is applied to the backprojected image. For the list-mode data, the backprojection is carried out in the event-by-event fashion, and a profile function may be used along the projection LOR. The 2D filter depends on the TOF timing resolution as well as the backprojection profile function. In order to emulate the iterative algorithm effects, a Fourier-domain window function is suggested. This window function has a parameter, k, which corresponds to the iteration number in an iterative algorithm.

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