Automatic object detection using shape information in ultrasound images

Cancela, Pablo; Reyes, Facundo; Rodríguez, Paul; Randall, Grégory; Fernández, Alicia

doi:10.1109/icip.2003.1247270

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…It also restricts the use of the image information to the interested region in this process. This band formation process is defined as Normal Mapping (NM), dependent on a curve C and is also termed as coordinate transformation [23]. The idea is to unroll the band around the curve in such a way that the horizontal coordinate (u) is the arc length of the curve and the vertical coordinate (v) is the distance to the curve.…”

Section: Normal Mappingmentioning

confidence: 99%

An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure

Rajendran¹,

Madheswaran²

2010

J Med Syst

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The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

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Section: Normal Mappingmentioning

confidence: 99%

An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure

Rajendran¹,

Madheswaran²

2010

J Med Syst

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“…Knowing that the shape of the object of interest in their images, the vessel wall, is circular, Zhu et al convert their image into polar coordinates and search for the line with the highest average intensity [5]. Cancela et al use a similar tactic, converting an given image into what they call shape coordinates, where a given model defines the x-axis of the shape coordinate system [4].…”

Section: Related Workmentioning

confidence: 99%

“…This shape information is usually either learnt from a set of training images [1,2,3], or incorporated from a given model [4] or assumption [5]. Other methods require user interaction to get an initial identification of the area of interest [6].…”

Section: Introductionmentioning

confidence: 99%

Towards Medical Ultrasound Image Segmentation with Limited Prior Knowledge

Booth

Patel

Lou

et al. 2006

2006 IEEE 12th Digital Signal Processing Workshop &Amp;amp; 4th IEEE Signal Processing Education Workshop

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Though a popular imaging technique, ultrasound is known for producing images filled with noise, distortions and shadowing effects. As a result, segmentation of ultrasound images require significant prior knowledge, often inserted into algorithms interactively or through shape information of the region of interest. This type of prior knowledge puts limitations on current approaches. This paper presents a different approach to ultrasound image segmentation that relies mainly on the physical properties of ultrasonic imaging. Robust intensity-based external energy formulations are incorporated into an Active Contour framework that is tolerant of the noise common to ultrasound images. By initializing the contour through an ellipse fitting procedure, an autonomous ultrasound image segmentation system is created that that can generalize to objects of varying shapes and sizes. The segmentation system was tested on ultrasound images of neonatal kidneys with results comparable to current methods.

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“…In [25], the authors show results of an algorithm for the automatic estimation of the rib-eye area using US images, but they do not explain the proposed method. In [2], Cancela et al proposed a method to solve this problem, that inspired our approach. The shape prior used in [2] was an ad hoc construction and, although the experimental database was more limited, the results were encouraging.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], Cancela et al proposed a method to solve this problem, that inspired our approach. The shape prior used in [2] was an ad hoc construction and, although the experimental database was more limited, the results were encouraging. We have compared that algorithm with the one proposed in this paper, using a much more extensive database and attained significantly better results.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Image Segmentation With Shape Priors: Application to Automatic Cattle Rib-Eye Area Estimation

Arias

Pini

Sanguinetti

et al. 2007

IEEE Trans. on Image Process.

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Abstract-Automatic ultrasound (US) image segmentation is a difficult task due to the quantity of noise present in the images and the lack of information in several zones produced by the acquisition conditions. In this paper, we propose a method that combines shape priors and image information to achieve this task. In particular, we introduce knowledge about the rib-eye shape using a set of images manually segmented by experts. A method is proposed for the automatic segmentation of new samples in which a closed curve is fitted taking into account both the US image information and the geodesic distance between the evolving curve and the estimated mean rib-eye shape in a shape space. This method can be used to solve similar problems that arise when dealing with US images in other fields. The method was successfully tested over a database composed of 610 US images, for which we have the manual segmentations of two experts.

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Automatic object detection using shape information in ultrasound images

Cited by 8 publications

References 7 publications

An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure

An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure

Towards Medical Ultrasound Image Segmentation with Limited Prior Knowledge

Ultrasound Image Segmentation With Shape Priors: Application to Automatic Cattle Rib-Eye Area Estimation

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