Effective Filtration Techniques for Gallbladder Ultrasound Images with Variable Contrast

Abstract

This paper presents a new method of filtering gallbladder contours on static ultrasound images. A major stage in the analysis of ultrasound images is to segment and section off areas occupied by the said organ. In the majority of cases this procedure is a key phase in the process of diagnosing pathological changes in tested organs. Unfortunately ultrasound images present among the most troublesome methods of analysis owing to the echogenic inconsistency of structures under observation. This also applies to the analysis of gallbladder images, chiefly targeted at recognizing changes, which may reveal evidence of developing inflammatory or cancerous changes. This paper provides for an inventive algorithm for the extraction of gallbladder image contours. The algorithm is based on rank filtration, as well as on the analysis of histogram sections on tested organs. The proposed approach of gallbladder image segmentation allows to obtain the effective results of contour detection which exceed 70%. This method is based on two procedures, which independently determine the relevant contour points for both correctly contrasted and vague ultrasound images. The independent results obtained from both procedures when merged allowed for a final determination of a tested organ's actual shape. Defined in such way morphology makes possible to perform a further classification, which is intended to define certain morbid changes such as inflammation, and cancer. The presented approach based on dynamically adjusted contrast improvement, expand traditional methods of ultrasound images segmentation by additional parallel filtration procedures. As shown in the paper such algorithm is easy scalable and effective.

Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions

“…A verification of the diagnosis algorithms, presented in sections 3 (a)-(e), was published in Ref. 8. The algorithms are based on an assumption that it is known in advance where exactly a gallbladder is located on an ultrasound image.…”

“…For this filtration we assume that each pixel on the image would be assigned a black colour, if in the vicinity of that pixel within the radius of 2 (pixels) the number of black pixels equals 13 or more. The value of 13 was defined empirically as well [8]. The other values would render the processed image either wholly black or white when ba− lanced [8].…”

“…The value of 13 was defined empirically as well [8]. The other values would render the processed image either wholly black or white when ba− lanced [8]. This processing is repeated tenfold (an empiri−…”

“…To develop effective algorithms, that would support an ultrasound separation and diagnostics of the gallbladder (GB) [8], it is necessary to provide such procedures with the information on: l situation of the organ on the ultrasound image, l location of the ultrasound source, l information on the perspective from which the organ was recorded. The information pertaining to the actual location of the ultrasound source is important for analyzing the surround− ings of GB.…”

Insider Threats and Cryptographic Techniques in Secure Information Management

“…Here, is an example of this process A formal grammar [9], [10], [12], [13] is defined for the selected class of systems which semantically analyze [4]- [6], company strategic data [11].…”