IZDVAJANJE ZNAČAJKI RENDGENSKIH SNIMAKA

Željko Knok1, Klaudio Pap2 , Mihael Kukec1

1Međimursko veleučilište u Čakovcu,
2Grafički fakultet Sveučilišta u Zagrebu

Sažetak

Analiza rendgenskih snimaka u medicinskoj praksi zauzima važno mjesto pri dijagnostici lomova. Za što precizniju dijagnostiku autori su kroz ovaj rad prezentirali izdvajanje značajki slike kroz primjer rendgenskog snimka. Potrebno je izdvojiti značajke slike na osnovu kojih se može interpretirati ili odrediti mjesto loma na slici. Najjednostavniji postupak za prepoznavanje lomova bio bi zansovan na usporedbi piksel-po-piksel između rendgenskog snimka prije i poslije loma dobivenih iz digitalne slike segmentacijom. Međutim, iz praktičnih razloga nije moguće, jer ne postoji pohranjen snimak prije loma. Odabran je veći broj značajki slike, a u cilju postizanja što boljih rezultata. Značajke pomoću kojih se mogu opisati slike, a koje se koriste u prepoznavanju dijelova slike su Eulerov broj, raspodjela gustoće bijelih piksela, brojanje rubova. Navedene značajke mogu poslužiti za bolju analizu slike, i ne predstavljaju konačno rješenje za određivanje mjesta loma na rendgenskom snimku.
Ključne riječi: Značajke slike, matlab, dijagnostika

Abstract

Analysis of X-ray imaging in medical practice has an important place in the diagnosis of fractures. The authors presented in this paper the extraction of image features on the example of x-ray images for the purposes of a more accurate diagnosis. It is necessary to extract the features of the image that can serve as a basis for interpretation or detection of fractures. The simplest procedure for identifying fractures would be a pixel-by-pixel comparison of the x-ray images made before and after fracture, obtained from a digital image by the means of segmentation. Moreover, the mentioned is not possible due to the lack of x-ray images before the fracture. More image features are selected in order to obtain best results as possible. Features which can describe images, used in recognition of parts of the images are Euler`s number, distribution of white pixels density, edges counting. Aforementioned features can serve for better analysis of images and do not represent the final solution for determining the fracture point on an x-ray image.
Key words: Image features, the MATLAB, diagnostics


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