https://www.cs.ucla.edu/~dt/papers/mia96/mia96.pdf

Deformable models in medical image analysis: a survey

We propose a method for detecting and describing features of faces using deformable templates. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parametr values can be used as descriptors for the feature. We illustrate this method by showing deformable templates detecting eyes and mouths in real images. We demonstrate their ability for tracking features.

Feature extraction from faces using deformable templates

Use of active shape models for locating structures in medical images

A method for detecting and describing the features of faces using deformable templates is described. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image, by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parameter values can be used as descriptors for the features. This method is demonstrated by showing deformable templates detecting eyes and mouths in real images. >

This paper describes a technique for building compact models of the shape and appearance of flexible objects (such as organs) seen in 2-D images The models are derived from the statistics of sets of labelled images of examples of the objects Each model consists of a flexible shape template, describing how important points of the object can vary, and a statistical model of the expected grey levels in regions around each model point The shape models are parameterised in such a way as to allow ‘legal’ configurations Such models have proved useful in a wide variety of applications We describe how the models can be used in local image search and give examples of their application to medical images We also describe how the method can be simply extended to segment 3-D objects in volume images and to track structures in image sequences

The Use of Active Shape Models for Locating Structures in Medical Images

We propose a method for detecting and describing features in medical images using deformable templates, for the purpose of diagnostic analysis of these features. The feature of interest can described by a parameterized template. An energy function is defined which links edges in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image content, by evaluating the energy function and accordingly altering its parameter values. A gradient maximization technique is used to optimize the placement and shape of the deformable template to fit the desired anatomical feature. The final parameter values can be used as descriptors for the feature. Measurements of intensity values within a region of the template can be used as inputs to a medical diagnostic system. We have developed a Picture Archive and Communication System (PACS) based image analysis program which employs the technique of deformable templates to localize features in dual energy CT images. Measurements can then be automatically made which can be used for maintenance of patients suffering from bone loss and abnormal marrow fat content. This system has been successfully tested on 552 (69 × 8) images and is currently in use at Massachusetts General Hospital, Boston, MA. Statistical comparisons between the system and previously used manual techniques show that their performances are practically equivalent and that the system has several advantages over the human operator, for example, consistency, accuracy and cost.

/pdf/deformable-templates-for-features-extraction-from-medical-ol6o3kj7wh.pdf

Deformable templates for features extraction from medical images

The authors propose a method for detecting and describing features in medical images by using deformable templates for the purpose of diagnostic analysis of these features. The feature of interest can be described by a parameterized template. A gradient maximization technique is used to change the parameters so that the template fits onto the desired anatomical feature. The final parameter values can be used as descriptors for the feature. Measurements of intensity values within a region of the template can be used as inputs to a medical diagnostic system. A picture-archive-and-communication-system-based image analysis program, which uses the technique of deformable templates to localize features in dual energy computer tomography (CT) images, is discussed. Statistical comparisons between the system and previously used manual techniques show that the performances are practically equivalent and that the system has several advantages over the human operator, for example, consistency, accuracy, and cost. >

P. Lipson

Papers

Deformable templates for features extraction from medical images

Automated bone density calculation using feature extraction by deformable templates