Literature DB >> 3336719

Effects of image processing on nodule detection rates in digitized chest radiographs: ROC study of observer performance.

R H Sherrier1, C Chiles, W E Wilkinson, G A Johnson, C E Ravin.   

Abstract

To evaluate the effects of image processing in digitized chest radiographs when high-resolution images are used, an examination was done in which the detection of pulmonary nodules in unprocessed digitized chest radiographs was compared with that in images that had undergone processing with two methods, adaptive filtration and histogram equalization. The processing techniques have been optimized in previous work to selectively enhance the retrocardiac and subdiaphragmatic areas without significant alteration of detail in the lung. Eight observers were shown 150 test radiographs (50 unprocessed, 50 processed with adaptive filtration, 50 processed with histogram equalization) containing 150 nodules. The results indicate a statistically significant (P less than .03) difference, with highest observer performance in the chest radiographs processed with adaptive filtration (median area under ROC curve = 0.78), compared with unprocessed images (median = 0.68) and chest radiographs processed with histogram equalization (median = 0.62). Performance in the lung was not significantly different. Adaptive filtration applied to selectively enhance underexposed areas of film images may improve nodule detection. Histogram equalization provided no improvement in performance.

Mesh:

Year:  1988        PMID: 3336719     DOI: 10.1148/radiology.166.2.3336719

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  7 in total

1.  Enhancement of chest images by automatic adaptive spatial filtering.

Authors:  M Souto; J Correa; P G Tahoces; D Tucker; K S Malagari; J J Vidal; R G Fraser
Journal:  J Digit Imaging       Date:  1992-11       Impact factor: 4.056

2.  Unsharp masking of low-dosed digital luminescence radiographs: results of a receiver operating characteristics analysis.

Authors:  R D Müller; M Voss; H Hirche; B Buddenbrock; V John; E Bosch
Journal:  Eur Radiol       Date:  1996       Impact factor: 5.315

Review 3.  Missed lung cancer: when, where, and why?

Authors:  Annemilia Del Ciello; Paola Franchi; Andrea Contegiacomo; Giuseppe Cicchetti; Lorenzo Bonomo; Anna Rita Larici
Journal:  Diagn Interv Radiol       Date:  2017 Mar-Apr       Impact factor: 2.630

4.  Context-dependent enhancements for radiological images.

Authors:  B Plessis; M Goldberg; R Dillon; J Tombaugh; J Robertson; G Bélanger; N Hickey
Journal:  J Digit Imaging       Date:  1989-05       Impact factor: 4.056

5.  Image optimization in a computed-radiography/photostimulable-phosphor system.

Authors:  R H Sherrier; H G Chotas; G A Johnson; C Chiles; C E Ravin
Journal:  J Digit Imaging       Date:  1989-11       Impact factor: 4.056

6.  Expert knowledge-infused deep learning for automatic lung nodule detection.

Authors:  Jiaxing Tan; Yumei Huo; Zhengrong Liang; Lihong Li
Journal:  J Xray Sci Technol       Date:  2019       Impact factor: 1.535

7.  Multi-scale Morphological Image Enhancement of Chest Radiographs by a Hybrid Scheme.

Authors:  Fatemeh Shahsavari Alavijeh; Homayoun Mahdavi-Nasab
Journal:  J Med Signals Sens       Date:  2015 Jan-Mar
  7 in total

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