Texture Analysis by Accurate Identification of Simple Markovian Models

Gimel’farb, Georgy; Farag, Aly A.

doi:10.1007/s10559-005-0038-3

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…While a high potential of malignancy is indicated by lower energy, high potential to be benign is indicated by higher energy. To calculate E 7 ( s ), the Gibbs potentials for the seventh-order model are calculated using the maximum likelihood estimates (MLE) by generalizing the analytical approximation in 21 , 22 :…”

Section: Methodsmentioning

confidence: 99%

A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules

Shaffie

Soliman

Fraiwan

et al. 2018

Technol Cancer Res Treat

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A novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: (1) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventh-order Markov Gibbs random field model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%.

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Section: Methodsmentioning

confidence: 99%

A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules

Shaffie

Soliman

Fraiwan

et al. 2018

Technol Cancer Res Treat

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“…To make the registration (almost) insensitive to these transformations, both the prototype and conforming to it part of each image are equalized using cumulative empirical probability distributions of their signals on R p . In line with a generic MGRF model with multiple pairwise interaction [18], the probability P (g) ∝ exp(E(g)) of an object g aligned with the prototype g • on R p is proportional to the Gibbs energy E(g) = |R p |V T F(g) where…”

Section: Mgrf Based Image Registrationmentioning

confidence: 96%

“…The approximate MLE of V is proportional to the scaled centered empirical co-occurrence distributions for the prototype [18]:…”

Section: Mgrf Based Image Registrationmentioning

confidence: 99%

Experiments on Robust Image Registration Using a Markov-Gibbs Appearance Model

El-Baz

Farag

Gimel’farb

2006

Lecture Notes in Computer Science

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Abstract.A new approach to align an image of a textured object with a given prototype under its monotone photometric and affine geometric transformations is experimentally compared to more conventional registration algorithms. The approach is based on measuring similarity between the image and prototype by Gibbs energy of characteristic pairwise co-occurrences of the equalized image signals. After an initial alignment, the affine transformation maximizing the energy is found by gradient search. Experiments confirm that our approach results in more robust registration than the search for the maximal mutual information or similarity of scale-invariant local features.

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“…The MLE of V is proportional in the first approximation to the scaled centered empirical co-occurrence distributions for the prototype [31]:…”

Section: Image Normalizationmentioning

confidence: 99%

A New Framework for Automatic Registration of 2D/3D Texture Images

El-Baz¹,

Gimel’farb²

2007

Procedings of the British Machine Vision Conference 2007

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Abstract. A new approach to align an image of a textured object with a given prototype is proposed. Visual appearance of the images, after equalizing their signals, is modeled with a Markov-Gibbs random field with pairwise interaction. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of pixel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by gradient search. Experiments confirm that our approach aligns complex 2D/3D objects better than popular conventional algorithms.

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Texture Analysis by Accurate Identification of Simple Markovian Models

Cited by 8 publications

References 23 publications

A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules

A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules

Experiments on Robust Image Registration Using a Markov-Gibbs Appearance Model

A New Framework for Automatic Registration of 2D/3D Texture Images

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