Minimum mean-square error transformations of categorical data to target positions

Zahorian, Stephen A.; Jagharghi, Amir J.

doi:10.1109/78.157177

Cited by 14 publications

(2 citation statements)

References 22 publications

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“…Therefore, maximizing the ratio of IED to I A D will be equivalent to minimizing IAD. Minimizing IAD, in turn, will be similar to minimizing the mean-square error between specified target positions and projections of the measurement data (13). That is, among all possible linear transformations that cluster normal tissues around prespecified locations, we select the one that generates the smallest intraset Euclidean distance for the normal tissue clusters in the transformed domain.…”

Section: Optimal Transformationmentioning

confidence: 99%

Feature Space Analysis of MRI

Soltanian‐Zadeh¹

2002

Signal Processing for Magnetic Resonance Imaging and Spectroscopy

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This paper presents an MRI feature-space image-analysis method and its application to brain tumor studies. The proposed method generates a transformed feature space in which the normal tissues (white matter, gray matter, and CSF) become orthonormal. As such, the method is expected to have site-to-site and patient-to-patient consistency, and is useful for identification of tissue types, segmentation of tissues, and quantitative measurements on tissues. The steps of the work accomplished are as follows: (1) Four T,-weighted and two T,-weighted images (before and after injection of gadolinium) were acquired for 10 tumor patients. (2) Images were analyzed by an image analyst according to the proposed algorithm. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image-analysis results were compared with the biopsy results. Pre-and postsurgery feature spaces were also compared. The proposed method made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. The method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre-with postsurgery and radiation feature spaces illustrated that the original solid tumor was not present in the second study, but a new tissue component appeared in a different location of the feature space. This tissue could be radiation necrosis generated as a result of radiation.

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Section: Optimal Transformationmentioning

confidence: 99%

Feature Space Analysis of MRI

Soltanian‐Zadeh¹

2002

Signal Processing for Magnetic Resonance Imaging and Spectroscopy

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“…Diese wurde schon mehrfach erfolgreich angewendet, um verschiedene Lautklassen zu normalisieren, z. B. von Klein et al (1970) und Zahorian und Jagharghi (1992). Hier geschieht das aber gewissermaßen für alle Klassen gleichzeitig.…”

Section: Die Spina-datenbasisunclassified

Steuerung sprechernormalisierender Abbildungen durch künstliche neuronale Netzwerke

Müller¹

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Feature space analysis of MRI

Soltanian‐Zadeh

Windham

Peck

et al. 1998

Magnetic Resonance in Med

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This paper presents an MRI feature-space image-analysis method and its application to brain tumor studies. The proposed method generates a transformed feature space in which the normal tissues (white matter, gray matter, and CSF) become orthonormal. As such, the method is expected to have site-to-site and patient-to-patient consistency, and is useful for identification of tissue types, segmentation of tissues, and quantitative measurements on tissues. The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of gadolinium) were acquired for 10 tumor patients. (2) Images were analyzed by an image analyst according to the proposed algorithm. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image-analysis results were compared with the biopsy results. Pre- and postsurgery feature spaces were also compared. The proposed method made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. The method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- with postsurgery and radiation feature spaces illustrated that the original solid tumor was not present in the second study, but a new tissue component appeared in a different location of the feature space. This tissue could be radiation necrosis generated as a result of radiation.

show abstract

Minimum mean-square error transformations of categorical data to target positions

Cited by 14 publications

References 22 publications

Feature Space Analysis of MRI

Feature Space Analysis of MRI

Steuerung sprechernormalisierender Abbildungen durch künstliche neuronale Netzwerke

Feature space analysis of MRI

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