Kidney segmentation in CT data using hybrid level-set method with ellipsoidal shape constraints
PBN-AR
Instytucja
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej (Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie)
Informacje podstawowe
Główny język publikacji
EN
Czasopismo
Metrology and Measurement Systems (20pkt w roku publikacji)
ISSN
0860-8229
EISSN
2300-1941
Wydawca
Polska Akademia Nauk
DOI
Rok publikacji
2017
Numer zeszytu
1
Strony od-do
101--112
Numer tomu
24
Link do pełnego tekstu
Identyfikator DOI
Liczba arkuszy
0.85
Autorzy
(liczba autorów: 4)
Pozostali autorzy
+ 2
Autorzy przekładu
(liczba autorów przekładu: 0)
Słowa kluczowe
EN
image segmentation
level set method
kidney
ellipsoid
CT data
Streszczenia
Język
EN
Treść
With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures ([e.g]. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the [Level Set] (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.
Cechy publikacji
original article
peer-reviewed
Inne
System-identifier
idp:104939
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