scholarly journals Shape of the Cochlear Basal Turn: An Indicator for an Optimal Electrode-to-Modiolus Proximity With Precurved Electrode Type

2020 ◽  
Vol 100 (1) ◽  
pp. 38-43
Author(s):  
Tawfiq Khurayzi ◽  
Anandhan Dhanasingh ◽  
Fida Almuhawas ◽  
Abdurrahman Alsanosi
Keyword(s):  
Three Dimensional ◽  
Electrode Array ◽  
Image Data ◽  
B Value ◽  
Data Sets ◽  
Basal Turn ◽  
A Value ◽  
Preoperative Image ◽  
Parameters Measurement ◽  
Positive Linear Relation

Objective: The objective of this study was to determine the shape of cochlear basal turn through basic cochlear parameters measurement. The secondary aim was to overlay an image of the precurved electrode array on top of the three-dimensional (3D) image of the cochlea to determine which shape of the cochlear basal turn gives optimal electrode-to-modiolus proximity. Materials and Methods: Computed tomography (CT) preoperative image-data sets of 117 ears were made available for the measurements of cochlear parameters retrospectively. Three-dimensional slicer was used in the visualization and measurement of cochlear parameters from both 3D and 2D (2-dimensional) images of the inner ear. Cochlear parameters including basal turn diameter ( A), width of the basal turn ( B), and cochlear height (H) were measured from the appropriate planes. B/ A ratio was made to investigate which ratios correspond to round and elliptical shape of the cochlear basal turn. Results: The cochlear size as measured by A value ranged between 7.4 mm and 10 mm. The B value and the cochlear height ( H) showed a weak positive linear relation with A value. The ratio between the B and A values anything above or below 0.75 could be an indicator for a more round- or elliptical shaped cochlear basal turn, respectively. One sized/shaped commercially available precurved electrode array would not offer a tight electrode-to-modiolus in the cochlea that has an elliptical shaped basal turn as identified by the B/A ratio of <0.75. Conclusion: Accurate measurement of cochlear parameters adds value to the overall understanding of the cochlear geometry before a cochlear implantation procedure. The shape of cochlear basal turn could have clinical implications when comes to electrode-to-modiolus proximity.

scholarly journals Diagnostic Value of 3D Segmentation in Understanding the Anatomy of Human Inner Ear Including Malformation Types

2020 ◽  
pp. 014556132090662
Author(s):  
Saad Alenzi ◽  
Anandhan Dhanasingh ◽  
Hani Alanazi ◽  
Abdulrahman Alsanosi ◽  
Abdulrahman Hagr
Keyword(s):  
Inner Ear ◽  
Three Dimensional ◽  
Internal Auditory Canal ◽  
B Value ◽  
Data Sets ◽  
Type Ii ◽  
3D Segmentation ◽  
Training Tool ◽  
A Value ◽  
Human Inner Ear

Objective: To understand the anatomical and dimensional variations of the human inner ear using 3-dimensional (3D) segmentation within the Middle East population. Design: Retrospective study. Setting: King Abdullah Ear Specialist Center (KAESC) Riyadh, Saudi Arabia. Participant: Forty computed tomography (CT) images of patients with sensorineural hearing loss who underwent cochlear implant (CI) were taken for analysis. Main outcome Measures: Three-dimensional images showing the anatomical variations of the inner ear including various pathological conditions, cochlear parameters including basal turn diameter (“A” value), “B” value which is perpendicular to “A” value, cochlear height, length, and width of the internal auditory canal (IAC), intercochlear spacing, and electrode angular insertion depth (AID). Results: Out of 40 CT image data sets, 12 had normal inner-ear anatomy (NA), 4 with enlarged vestibular aqueduct syndrome (EVAS), 8 with only 2 turns of the cochlea (2TL), 7 with incomplete partition (IP) type II, 5 with cochlear hypoplasia, 1 with common cavity, and 3 with abnormal IAC. Taking the NA, EVAS, 2TL, and the IP type II cases altogether, age of the patient had no correlation with the “A” value; however, the “A” value had a linear correlation with the “B” value. The age of the patient had an increasing logarithmic correlation with the IAC length and the intercochlear spacing. The “A” value did not have any meaningful correlation with the cochlear height. Three data sets showed asymmetric inner-ear malformation types on either side of the ears. All these 40 cases were implanted with various CI electrode array variants and the corresponding postoperative plain film X-ray images showing the electrode AID are given separately in figures. Conclusions: Three-dimensional segmentation of the inner ear from the temporal bone CT is a valuable clinical and training tool for surgeons and radiologists especially in difficult cases which will certainly help to understand the overall anatomical and dimensional variations.

scholarly journals Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tzu-Ching Wu ◽  
Xu Wang ◽  
Linlin Li ◽  
Ye Bu ◽  
David M. Umulis
Keyword(s):  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Image Data ◽  
True Positive Rate ◽  
Fine Tuning ◽  
Data Sets ◽  
Nuclei Segmentation ◽  
Robust Segmentation ◽  
Positive Rate ◽  
Segmentation Image

AbstractIdentification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.

scholarly journals Three-Dimensional Quantitative Autoradiography by Disparity Analysis: Theory and Application to Image Averaging of Local Cerebral Glucose Utilization

1995 ◽  
Vol 15 (4) ◽  
pp. 552-565 ◽  
Author(s):  
Weizhao Zhao ◽  
Myron D. Ginsberg ◽  
David W. Smith
Keyword(s):  
Standard Deviation ◽  
Glucose Utilization ◽  
Three Dimensional ◽  
Image Data ◽  
Data Sets ◽  
Local Cerebral Glucose Utilization ◽  
Entire Series ◽  
Cerebral Glucose Utilization ◽  
3D Data ◽  
Autoradiographic Image

Traditional autoradiographic image analysis has been restricted to the two-dimensional assessment of local cerebral glucose utilization (LCMRglc) or blood flow in individual brains. It is advantageous, however, to generate an entire three-dimensional (3D) data set and to develop the ability to map replicate images derived from multiple studies into the same 3D space, so as to generate average and standard deviation images for the entire series. We have developed a novel method, termed “disparity analysis,” for the alignment and mapping of autoradiographic images. We present the theory of this method, which is based upon a linear affine model, to analyze point-to-point disparities in two images. The method is a direct one that estimates scaling, translation, and rotation parameters simultaneously. Disparity analysis is general and flexible and deals well with damaged or asymmetric sections. We applied this method to study LCMRglc in nine awake male Wistar rats by the [14C]2-deoxyglucose method. Brains were physically aligned in the anteroposterior axis and were sectioned subserially at 100-μm intervals. For each brain, coronal sections were aligned by disparity analysis. The nine brains were then registered in the z-axis with respect to a common coronal reference level (bregma + 0.7 mm). Eight of the nine brains were mapped into the remaining brain, which was designated the “template,” and aggregate 3D data sets were generated of the mean and standard deviation for the entire series. The averaged images retained the major anatomic features apparent in individual brains but with some defocusing. Internal anatomic features of the averaged brain were smooth, continuous, and readily identifiable on sections through the 3D stack. The fidelity of the internal architecture of the averaged brain was compared with that of individual brains by analysis of line scans at four representative levels. Line scan comparisons between corresponding sections and their template showed a high degree of correlation, as did similar comparisons performed on entire sections. Fourier analysis of line scan data showed retention of low-frequency information with the expected attenuation of high-frequency components produced by averaging. Region-of-interest (ROI) analysis of the averaged brain yielded LCMRglc values virtually identical to those derived from measurements and subsequent averaging of data from individual brains. In summary, 3D reconstruction of averaged autoradiographic image data by disparity analysis is a feasible approach, which vastly simplifies ROI analysis, facilitates the assessment of hemodynamic or metabolic patterns in three dimensions, permits the computation of threshold-defined volumes of interest on averaged 3D data sets, makes possible atlas-based ROI strategies, and importantly provides the capability of generating 3D image data sets derived from arithmetic manipulations on two or more primary data sets (e.g., percent difference or ratio images).

scholarly journals Layered manufacturing for medical imaging data

2019 ◽  
Vol 11 (8) ◽  
pp. 168781401987139
Author(s):  
Shyh-Kuang Ueng ◽  
Hsin-Cheng Huang ◽  
Chieh-Shih Chou ◽  
Hsuan-Kai Huang
Keyword(s):  
Medical Image ◽  
Three Dimensional ◽  
Image Data ◽  
Target Object ◽  
Layered Manufacturing ◽  
Boundary Representation ◽  
Data Sets ◽  
Imaging Data ◽  
Distance Field ◽  
Dimensional Image

Layered manufacturing techniques have been successfully employed to construct scanned objects from three-dimensional medical image data sets. The printed physical models are useful tools for anatomical exploration, surgical planning, teaching, and related medical applications. Before fabricating scanned objects, we have to first build watertight geometrical representations of the target objects from medical image data sets. Many algorithms had been developed to fulfill this duty. However, some of these methods require extra efforts to resolve ambiguity problems and to fix broken surfaces. Other methods cannot generate legitimate models for layered manufacturing. To alleviate these problems, this article presents a modeling procedure to efficiently create geometrical representations of objects from computerized tomography scan and magnetic resonance imaging data sets. The proposed procedure extracts the iso-surface of the target object from the input data set at the first step. Then it converts the iso-surface into a three-dimensional image and filters this three-dimensional image using morphological operators to remove dangling parts and noises. At the next step, a distance field is computed in the three-dimensional image space to approximate the surface of the target object. Then the proposed procedure smooths the distance field to soothe sharp corners and edges of the target object. Finally, a boundary representation is built from the distance field to model the target object. Compared with conventional modeling techniques, the proposed method possesses the following advantages: (1) it reduces human efforts involved in the geometrical modeling process. (2) It can construct both solid and hollow models for the target object, and wall thickness of the hollow models is adjustable. (3) The resultant boundary representation guarantees to form a watertight solid geometry, which is printable using three-dimensional printers. (4) The proposed procedure allows users to tune the precision of the geometrical model to compromise with the available computational resources.

scholarly journals The b-value of the Kelud Volcano in the Last Three Decades

2021 ◽  
Vol 325 ◽  
pp. 01019
Author(s):  
Sintia Windhi Niasari ◽  
Lusia Rita Nugraheni ◽  
Puspita Dian Maghfira
Keyword(s):  
Explosive Eruption ◽  
Total Population ◽  
B Value ◽  
Data Sets ◽  
Tourist Destination ◽  
Effusive Eruption ◽  
A Value ◽  
Hazard Risk ◽  
Seismicity Monitoring

Kelud volcano is located in the Kediri sub-district, East Java Province, Indonesia. This volcano is still active, with total population, in the radius of 10 km, is around 10 thousand people. Kelud volcano is a popular tourist destination. On the weekend, total visitor can reach 5,000 people per-day. These people are at high risk when the Kelud volcano erupts. The last eruption of the Kelud volcano occurred in 2014 and was explosive eruption. Previously, there was an effusive eruption in 2007. These two types of eruption have its own geo hazard risk. Thus, predict the eruption type could help hazard mitigation. In this study, two data sets of earthquakes, 1990-2007 and 2008-2020, were analysed to determine the b-value and its relationship to the eruption type of the Kelud volcano. The calculation of the b-value uses the Gutenberg-Richter relationship. Calculation of the b-value in 2007, when there was an effusive eruption, showed a value of 2.27, while in 2014 (when there was an explosive eruption) was 1.85. After 2009, the curve of the b-value against time shows decrease. As a long term precursor of the Kelud activity, this b-value curve should be analysed continuously, besides volcano tectonic seismicity monitoring.

scholarly journals WaveletSEG: Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

2020 ◽  
Author(s):  
Tzu-Ching Wu ◽  
Xu Wang ◽  
Linlin Li ◽  
Ye Bu ◽  
David M. Umulis
Keyword(s):  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Image Data ◽  
True Positive Rate ◽  
Fine Tuning ◽  
Data Sets ◽  
Nuclei Segmentation ◽  
Robust Segmentation ◽  
Positive Rate ◽  
Segmentation Image

AbstractIdentification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio (SNR). The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.

Wavelet Compression of Three-Dimensional Time-Lapse Biological Image Data

2005 ◽  
Vol 11 (1) ◽  
pp. 9-17 ◽  
Author(s):  
H. Narfi Stefansson ◽  
Kevin W. Eliceiri ◽  
Charles F. Thomas ◽  
Amos Ron ◽  
Ron DeVore ◽  
...  
Keyword(s):  
High Speed ◽  
Three Dimensional ◽  
Image Data ◽  
Large Data ◽  
Time Lapse ◽  
Large Data Sets ◽  
Multidimensional Data ◽  
Data Sets ◽  
Optical Sectioning ◽  
Dynamic Events

The use of multifocal-plane, time-lapse recordings of living specimens has allowed investigators to visualize dynamic events both within ensembles of cells and individual cells. Recordings of such four-dimensional (4D) data from digital optical sectioning microscopy produce very large data sets. We describe a wavelet-based data compression algorithm that capitalizes on the inherent redunancies within multidimensional data to achieve higher compression levels than can be obtained from single images. The algorithm will permit remote users to roam through large 4D data sets using communication channels of modest bandwidth at high speed. This will allow animation to be used as a powerful aid to visualizing dynamic changes in three-dimensional structures.

Knee Bone Surface Registration Using the Lipschitz Optimization Algorithm

2012 ◽  
Vol 443-444 ◽  
pp. 537-541
Author(s):  
Xiao Peng Wang ◽  
Yuan Zhi Cheng ◽  
Ming Ming Zhao ◽  
Xiao Hua Ding ◽  
Jing Bai
Keyword(s):  
Search Algorithm ◽  
Three Dimensional ◽  
Image Data ◽  
Surface Registration ◽  
Data Sets ◽  
Bone Surface ◽  
Registration Accuracy ◽  
Mr Image ◽  
Lipschitz Optimization ◽  
Animal Data

We describe a technique for the registration of three dimensional (3D) knee bone surface points from MR image data sets. This technique is grounded on a mathematical theory – Lipschitz optimization. Based on this theory, we propose a global search algorithm that simultaneously determines the transformation and point correspondences. Compared with the other three registration approaches (ICP, EM-ICP, and genetic algorithms), the new proposed method achieved the highest registration accuracy on animal data.

scholarly journals AUTOMATED ALIGNMENT OF LOCAL POINT CLOUDS IN DIGITAL BUILDING MODELS

2019 ◽  
Vol XLII-5/W2 ◽  
pp. 35-39
Author(s):  
T. Kaiser ◽  
C. Clemen ◽  
H.-G. Maas
Keyword(s):  
Three Dimensional ◽  
Image Data ◽  
Point Clouds ◽  
Data Sets ◽  
Control Points ◽  
Building Models ◽  
Local Point ◽  
Synthetic Test ◽  
Transformation Parameters ◽  
Automated Alignment

<p><strong>Abstract.</strong> For the correct usage and analysis within a BIM environment, image-based point clouds that were created with Structure from Motion (SfM) tools have to be transformed into the building coordinate system via a seven parameter Helmert Transformation. Usually control points are used for the estimation of the transformation parameters. In this paper we present a novel, highly automated approach to calculate these transformation parameters without the use of control points. The process relies on the relationship between wall respectively plane information of the BIM and three-dimensional line data that is extracted from the image data. In a first step, 3D lines are extracted from the oriented input images using the tool Line3D++. These lines are defined by the 3D coordinates of the start and end points. Afterwards the lines are matched to the planes originating from the BIM model representing the walls, floors and ceilings. Besides finding a suitable functional and stochastic model for the observation equations and the adjustment calculation, the most critical aspect is finding a correct match for the lines and the planes. We therefore developed a RANSAC-inspired matching algorithm to get a correct assignment between elements of the two data sources. Synthetic test data sets have been created for evaluating the methodology.</p>

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