Process Plant & Equipment--Cost Estimation

Summarizes origins and methodology of VE. L. D. Miles is listed in references at end of article. Author notes that he owes his "'initiation' into VE to Mr. R. H. Kempter and Mr. R. H. Rossman...during their visit to India.

Process plant & equipment cost estimation

+235hlm.;27c

Corporate failure: prediction, panacea and prevention/ Kharbanda

xvii, 224 hal.; 23,5 cm

Corporate failure: prediction, panacea and prevention/ Kharbanda

xvii, 224 hal.; 23,5 cm

A pilot study for segmentation of pharyngeal and sino-nasal airway subregions by automatic contour initialization

Purpose The objective of the present study is to put forward a novel automatic segmentation algorithm to segment pharyngeal and sino-nasal airway subregions on 3D CBCT imaging datasets. Methods A fully automatic segmentation of sino-nasal and pharyngeal airway subregions was implemented in MATLAB programing environment. The novelty of the algorithm is automatic initialization of contours in upper airway subregions. The algorithm is based on boundary definitions of the human anatomy along with shape constraints with an automatic initialization of contours to develop a complete algorithm which has a potential to enhance utility at clinical level. Post-initialization; five segmentation techniques: Chan-Vese level set (CVL), localized Chan-Vese level set (LCVL), Bhattacharya distance level set (BDL), Grow Cut (GC), and Sparse Field method (SFM) were used to test the robustness of automatic initialization. Results Precision and F-score were found to be greater than 80% for all the regions with all five segmentation methods. High precision and low recall were observed with BDL and GC techniques indicating an under segmentation. Low precision and high recall values were observed with CVL and SFM methods indicating an over segmentation. A Larger F-score value was observed with SFM method for all the subregions. Minimum F-score value was observed for naso-ethmoidal and sphenoidal air sinus region, whereas a maximum F-score was observed in maxillary air sinuses region. The contour initialization was more accurate for maxillary air sinuses region in comparison with sphenoidal and naso-ethmoid regions. Conclusion The overall F-score was found to be greater than 80% for all the airway subregions using five segmentation techniques, indicating accurate contour initialization. Robustness of the algorithm needs to be further tested on severely deformed cases and on cases with different races and ethnicity for it to have global acceptance in Katradental radKatraiology workflow

Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm

PURPOSE: To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. METHODS: The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. RESULTS: Inter-observer ICC for each landmark was found to be excellent ([Formula: see text]) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm ([Formula: see text] distance) and [Formula: see text] ([Formula: see text]-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and [Formula: see text]). CONCLUSION: Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification

Automatic Landmark Identification in Lateral Cephalometric Images Using Optimized Template Matching

Cephalometric analysis has long helped researchers and orthodontic practitioners for evaluation of facial growth, understanding facial morphology and its ethnic variations, orthodontic diagnosis and treatment planning for patients presenting with malocclusion and dentofacial deformities. Mostly, inaccuracy in cephalometric measurements is a reflection of errors in identification and accurate localization of anatomical landmarks. The accuracy of landmark identification is greatly influenced by knowledge of the operator and experience. Moreover, the process of manual detection is tedious and time consuming. Therefore, a need for development of robust and accurate algorithms for automatic detection of landmarks on cephalometric images has been comprehended. In this work, we hereby propose an optimized template matching (OTM) algorithm which could automatically localize hard and soft tissue anatomical landmarks on lateral cephalometric images. This algorithm was tested for sixteen hard and eight soft tissue landmarks chosen in 12 regions on 37 lateral cephalograms obtained from subjects of either sex covering wide spectrum of malocclusion cases. The results of proposed automatic algorithm were compared to that of manual marking conducted by three experienced orthodontic specialists. All the 24 landmarks (100%) were detected within 3.0 mm error range of manual marking, 23 (96%) were detected within 2.5 mm error range and 16 (66.6%) landmarks were detected within 2.0 mm error range. The optimized template matching (OTM) algorithm may prove to be a promising approach in automatic detection of anatomical landmarks on cephalometric images

Effective project cost control

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