magistrsko delo
Doroteja Plavčak (Author), Nejc Mekiš (Reviewer), Janez Žibert (Mentor), Erna Huskić (Co-mentor)

Abstract

Uvod: Mamografija je osnovna diagnostična metoda za odkrivanje bolezenskih nepravilnosti dojk. Doseganje optimalne kakovostne slike pri mamografiji je za radiološkega inženirja tehnično zelo zahtevno. Redno ocenjevanje kvalitete mamografskih slik omogoča sprotno odkrivanje napak in iskanje rešitev za odpravo le-teh. Ker je lahko ocenjevanje slik zamudno, hkrati pa je ocenjevanje dokaj subjektivno, bi uporaba avtomatskih postopkov segmentacije precej skrajšala celoten proces. Segmentacija je postopek določitve homogenega področja (ROI) in razmejitev le-tega od ozadja. V osnovi ločimo segmentacijske metode na postopke določanja homogenih območij na sliki in na postopke določanja robov med območji. Namen: Namen magistrske naloge je teoretični pregled obstoječih postopkov segmentacije po posameznih vrstah diagnostičnega postopka. Z željo po postavitvi programa za avtomatsko ocenjevanje mamografskih slik smo v nalogi izvedli prvi korak, in sicer smo izvedli postopke segmentacije za detekcijo določenih elementov, ki se uporabljajo za ocenjevanje radioloških inženirjev na programu DORA. Na podlagi testiranja na bazi slik smo določili, kako uspešna je uporaba avtomatskih in polavtomatskih postopkov segmentacije za detekcijo prsne mišice na slikah. Metode dela: Na 250 mamografskih MLO slikah smo najprej ročno označili pektoralno mišico in dojko. Nato smo izvedli avtomatične postopke segmentacije, s katerimi smo poskušali čim bolj natančno označiti pektoralno mišico in dojko. Za označevanje dojke smo uporabili upragovljanje, za segmentacijo pektoralne mišice pa metodo povečevanja območij in rojenje. Predvsem nas je zanimala razlika v ploščinah krivulj med dobljeno in referenčno sliko. Rezultati: Z metodo upragovljanja smo ločili ozadje od objekta. Prišlo je do minimalnih odstopanj, in sicer je povprečna vrednost deleža relativne napake znašala 1,13 %. Pri segmentaciji pektoralne mišice je prišlo do večjih napak. Pri metodi povečevanja območij je povprečna vrednost deleža relativne napake znašala 22,83 %, pri metodi rojenja pa 32,7 %. Razprava in zaključek: Zaradi velike razlike v kontrastnosti med ozadjem in objektom, torej celotno dojko, je upragovljanje primerno za segmentacijo celotne dojke. Če primerjamo metodi za segmentacijo pektoralne mišice, je ustreznejša metoda povečevanja območij. Do večjih odstopanj prihaja zaradi tehnično neustrezne slike in večje količine žleznega tkiva.

Keywords

magistrska dela;radiološka tehnologija;mamografija;DORA;pektoralna mišica;segmentacija;upragovljanje;metoda povečevanja območij;rojenje;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL ZF - University College of Health Studies
Publisher: [D. Plavčak]
UDC: 616-07
COBISS: 27328003 Link will open in a new window
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Other data

Secondary language: English
Secondary title: Comparison of the efficiency of automatic segmentation of pectoral muscle
Secondary abstract: Introduction: Mammography has recently become the main diagnostic method for detecting breast abnormalities. Achieving an optimal image quality in mammography is very technically difficult for radiologic technologist. With regular assessment of the quality of mammographic images technical errors can be discovered and improved. However, since image estimation can be time consuming, and at the same time estimation is quite subjective, the use of automatic segmentation would significantly shorten the whole process. Segmentation is the process of determining a homogeneous region of interest (ROI) and demarcating it from the background. Basically, we divide the segmentation methods into methods, that are based on combining the pixels into homogeneous areas and the methods based on determining the edges between areas. Purpose: The purpose of the master's thesis is a theoretical overview of existing segmentation methods by individual types of diagnostic procedure. In the next step we will try to perform segmentation procedures for the detection of certain elements used to evaluate radiological engineers in the DORA program. Based on image testing, we will determine how successful the use of automatic and semi-automatic segmentation procedures in detection of pectoral muscle in images is. Methods: in 250 mammographic MLO images, we will first manually mark the breast and the pectoral muscle. Then we will perform the automatic segmentation of breast and pectoral muscle. We will use thresholding to mark the breast and the region growing and clustering for segmentation of the pectoral muscle. We will be mainly interested in the difference in the areas of the curves between the obtained and the reference image. Results: We used thresholding to separate the background from the object. There were minimal deviations, namely the average value of the relative error rate was 1.13 %. Bigger errors occurred in pectoral muscle segmentation. In the method of region growing, the average value of the relative error rate is 22,83 %, and in the method of clustering 32,7 %. Discussion and conclusion: Due to a big difference in contrast between the background and the breast, the thresholding is suitable for segmentation of the entire breast. However, if we compare the methods for segmentation of the pectoral muscle, the region growing method is more appropriate. Major deviations occur due to a technically inadequate picture and a larger amount of glandular tissue.
Secondary keywords: master's theses;radiologic technology;mammography;DORA;pectoral muscle;segmentation;thresholding;region growing;clustering;
Type (COBISS): Master's thesis/paper
Study programme: 0
Thesis comment: Univ. v Ljubljani, Zdravstvena fak., Oddelek za radiološko tehnologijo
Pages: 45 str., [3] str. pril.
ID: 12025918