Seed Point Detection of Multiple Cancers Based on Empirical Domain Knowledge and K-means in Ultrasound Breast Image

Koo, Lock-Jo; Ko, Min Soo; Jo, Hee-Won; Park, Sang-Chul; Wang, Gi-Nam

doi:10.1007/978-3-642-05253-8_56

Cited by 1 publication

(1 citation statement)

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“…As manually selecting seed points can be cumbersome, some studies have proposed automatic seed point detection, such as (Koo et al, 2009). They assumed the pixel value of a tumour image, after undergoing image enhancement, is close to zero.K-means clustering was used for automatic detection of the seed point, thus the grayscale value of the pixels (which assumed to be closed to zero) for the tumour region and the coordinate of the image pixels were considered for the K-mean.…”

Section: Jcsmentioning

confidence: 99%

Cavity Segmentation and Stroke Volume Measurement Methods From Echocardiographic Images: Review

Abboud¹,

Rahmat²,

Kadiman³

et al. 2014

Journal of Computer Science

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The complexity and criticality of heart disease diagnosis and treatment have frequently attracted the attention of researchers to develop approaches that can handle accurate clinical assessments. One of these complexities is measuring the Right Ventricle stroke volume. Therefore, different cardiac imaging techniques have been developed to help specialists in their regular clinical work and in the operating theatre to assist in the diagnosis, monitoring of the treatment and to ensure the success of a cardiac intervention as far as possible. Echocardiography is one of the cardiac imaging techniques and is considered as the most effective imaging technique as it is minimally invasive, quick and the cheapest method. Many studies have been conducted on Right Ventricle assessment from echocariographic images. This review is aimed at presenting the previous technical elements and algorithms used to measure the ventricle stroke volume from echocardiographic images. The review focuses on the five technical approaches that have been used, which are pre-processing, End-Distally (ED) and End-Systolic (ES) frame detection, cavity segmentation, the stoke volume measurement and 3D reconstruction. Finally, a summary will conclude this study.

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Section: Jcsmentioning

confidence: 99%