A methodology for the validation of image segmentation methods

Graaf, Cornelis N. de; Koster, André S. E.; Vincken, Koen L.; Viergever, Max A.

doi:10.1109/cbms.1992.244967

Cited by 16 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[8,9]). Many of the researchers who do evaluate their segmentation algorithms do so only on a limited number of components, such as cost analysis [10], inter-rater reliability [11], overall volume [12], or the Hausdorff distance [13]. These efforts, despite representing a valid attempt at evaluation, exemplify the difficulty in devising comprehensive and effective segmentation evaluation methodologies in this domain.…”

Section: Introductionmentioning

confidence: 99%

A framework for evaluating image segmentation algorithms

Udupa

LeBlanc

Zhuge

et al. 2006

Computerized Medical Imaging and Graphics

356

263

View full text Add to dashboard Cite

The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors-precision (reliability), accuracy (validity), and efficiency (viability)-need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit, repeat segmentation considering all sources of variation, and determine variations in figure of merit via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. In determining accuracy, it may be important to consider different 'landmark' areas of the structure to be segmented depending on the application. To assess efficiency, both the computational and the user time required for algorithm training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency factors have an influence on one another. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors, as illustrated in an example wherein two methods are compared in a particular application domain. The weight given to each factor depends on application. q

show abstract

Section: Introductionmentioning

confidence: 99%

A framework for evaluating image segmentation algorithms

Udupa

LeBlanc

Zhuge

et al. 2006

Computerized Medical Imaging and Graphics

356

263

View full text Add to dashboard Cite

show abstract

“…Como criterio de evaluación, se ha utilizado el error cuadrático medio, en donde se han comparado los resultados de los dos métodos de segmentación implementados con la imagen segmentada por el software especializado. En la ecuación (1) se puede apreciar la expresión matemática del criterio seleccionado [9]: En la tabla 5 se pueden apreciar los resultados del error medio cuadrático del método de segmentación de niveles de grises para varios umbrales seleccionados.…”

Section: Comparacion De Resultadosunclassified

Métodos de segmentación de nubes en imágenes satelitales

Vargas

Neira

Arango³

2013

View full text Add to dashboard Cite

El presente artículo tiene como objetivo mostrar los resultados de la aplicación de dos técnicas de segmentación de nubes en imágenes satelitales GOES; la primera, es una técnica basada en regiones, la umbralización por niveles de grises; y la segunda, es una técnica basada en fronteras la Transformada Hough. Finalmente, se comparan los resultados encontrados por los dos métodos con la segmentación obtenida a partir de un software especializado en imágenes satelitales mediante la separación de la banda espectral con la información de interés.

show abstract

“…The choice of segmentation algorithms is a key preliminary step to determining shape similarity. [5][6][7][8][9][10][11] All these factors of shape variability suggest that a certain level of uncertainty must be permitted between the stored template and the extracted object when constructing an automatic object recognition algorithm.…”

Section: Introductionmentioning

confidence: 99%

Remotely sensed object recognition using multiple comparative measures

Power

2004

Image and Signal Processing for Remote Sensing IX

View full text Add to dashboard Cite

In the real world of remote sensing, rarely does the extracted object precisely match a stored template of that object. A certain level of uncertainty must be permitted between the stored template and the extracted object. One solution to deal with this uncertainty is to evaluate measures between the object and template. Many measures have been introduced independently in literature for evaluating the statistical nature of the extracted objects such as a variety of shape and texture measures. The object is measured and compared to similar measures taken from the template. This paper suggests using measures extracted through a joint comparative process of template and object. It also suggests using multiple measures from the joint class of measures as opposed to using an individual measure to determine the sufficiency of the match. In particular, this paper demonstrates the value of using multiple comparative shape measures as opposed to one particular shape measure to achieve confidence in a match. The multiple-shape measure approach uses a matched filter measure, a Procrustes metric, a partial-direct hausdorf measure, and percent-pixels same measure. Each shape measure gives slightly different insight about the shape comparison which allows more confidence in the match. An experimental result is given that demonstrates the implementation and usefulness of the multiple comparative measure approach for recognizing objects from remotely sensed imagery.

show abstract

A methodology for the validation of image segmentation methods

Cited by 16 publications

References 16 publications

A framework for evaluating image segmentation algorithms

A framework for evaluating image segmentation algorithms

Métodos de segmentación de nubes en imágenes satelitales

Remotely sensed object recognition using multiple comparative measures

Contact Info

Product

Resources

About