Comparison between Elliptical and Squared ROI to Launch an Automatic Seed to Region Growing Algorithm on Hepatic Segmentation using CT images

Anastácio, Rogério; Thomaz, R. L.; Moraes, C. R. de; Almeida, G.; Ferreira, T. O.; Cândido, Marcelo; Macedo, Thiago Andrade; Patrocinio, Ana Claudia

doi:10.1007/978-3-319-19387-8_54

Cited by 1 publication

(2 citation statements)

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“…Gaidel [Gaidel (2017)] proposed automatic region of interest selection method in lungs which is based on a greedy forward selection using local texture features as an indicator of similarity with regions of interest from the learning sample. Anastácio et al [Anastácio, Thomaz, de Moraes et al (2015)] proposed a technique which defines the Seed Launch on Elliptical Area (SLEA) for find the region of interest, and the second technique defines the Seed Launch into Square Area (SLSA) for find the region of interest. Parametric Active contours [Kass, Witkin and Terzopoulos (1988)] have been applied often for segmentation and region of interest detetion of medical images and its variational reformulation as geometric active contours [Caselles, Kimmel and Sapiro (1995)] offer more flexibility during the region of interest detection process with regards to topological changes.…”

Section: Related Workmentioning

confidence: 99%

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Novel Approach for Automatic Region of Interest and Seed Point Detection in CT Images based on Temporal and Spatial Data

Liu¹,

Maere²,

Song³

2019

Computers, Materials &Amp; Continua

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Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) images based on temporal and spatial data. Our method is a 3 stages approach, 1) We extract organ features from the CT images by adopting the Hounsfield filter. 2) We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours' area and automatically detect a seed point. 3) We use a novel approach to track the growing region changes across the CT image sequence in detecting region of interest, given a seed point as our input. We used quantitative and qualitative analysis to measure the accuracy against the given ground truth and our results presented a better performance than other generic approaches for automatic region of interest detection of organs in abdominal CT images. With the results presented in this research work, our proposed novel sequence approach method has been proven to be superior in terms of accuracy, automation and robustness.

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Section: Related Workmentioning

confidence: 99%

“…We evaluated our approach and compared with two other major approaches: Seed Launch on Elliptical Area (SLEA) and Seed Launch into Square Area (SLSA) [Anastácio, Thomaz, de Moraes et al (2015)]. Using mean square error after apply the output region of interest to classical segmentation technique called Region Growing.…”

Section: Mean Square Errormentioning

confidence: 99%