Automatic detection of lung nodules: application to radiogram lossy coding

Michela Casaldi; Giuseppe Russo; Gaetano Scarano; Paolo Talone

doi:10.1117/12.310867

24 June 1998 Automatic detection of lung nodules: application to radiogram lossy coding

Michela Casaldi, Giuseppe Russo, Gaetano Scarano, Paolo Talone

Proceedings Volume 3338, Medical Imaging 1998: Image Processing; (1998) https://doi.org/10.1117/12.310867
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

In this paper, adaptive lossy coding procedures are applied to 12 bit/pixel lung radiograms, with the general goal to achieve high compression ratios while preserving the diagnostic information. To grant the diagnostic accuracy, regions that are classified as significant from a pathological point of view, have been preserved in the coding process. The extraction of relevant regions (nodules) has been performed automatically by the use of suitable operators based on mathematical morphology. Image structures, are classified in different categories, to treat the `generic' ones, peculiarly bony structures prevailing in this kind of images, differently from those having a diagnostic interest, because joined to specific pathologies. The last version of the JPEG sequential coding scheme, that allows variable quantization, has been used to code the images. This procedure is based on the DCT transform applied to 8 X 8 image blocks followed by adaptive quantization of the transformed blocks and then by an entropy coding of the quantized coefficients. As the information loss is due to the quantization operation, blocks containing `interesting' structures are quantized using smaller steps.

Citation Download Citation

Michela Casaldi, Giuseppe Russo, Gaetano Scarano, and Paolo Talone "Automatic detection of lung nodules: application to radiogram lossy coding", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); https://doi.org/10.1117/12.310867

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available