Abhishek Midya

Automatic characterization of masses in mammograms

The classification of benign and malignant masses in digital mammogram is an important yet challenging step for the early detection of breast cancer. This paper presents statistical measures of the orientation of texture to classify malignant and benign masses. Since the presence of mass in mammogram may change the orientation of normal breast tissues, two types of co-occurrence matrices are derived to estimate the joint occurrence of the angles of oriented structures for characterizing them. Haralicks 14 features are then extracted from each of the matrices derived from different regions related to mass. A total of 444 mass regions from 434 scanned-film images of the DDSM database are selected to evaluate the performance of the proposed features to differentiate the masses. The features are also compared with Haralicks features, obtained from well-known gray-level co-occurrence matrix. The best Az value of 0.77 is achieved with the stepwise logistic regression method for feature selection, an Fisher linear discriminant analysis for classification, and the leave-one-ROI-out approach for cross validation.

Classification of benign and malignant masses in mammograms using multi-resolution analysis of oriented patterns

The paper proposes a novel approach for the classification of breast masses as benign and malignant using multi-resolution analysis of oriented patterns of tissues in mammograms. Since, the oriented structures of normal breast near the mass region may be changed in presence of masses, three regions are defined, first, for the analysis. Statistical features are then extracted using two angle co-occurrence matrices derived at different resolution levels of each region with Haar-wavelet transform to quantify the joint occurrences of different angle pairs of oriented patterns. The experiments show best classification accuracy of 81.23% and area under the receiver operating characteristic curve of 0.86 with 433 images from the DDSM database using artificial neural network and tenfold cross-validation method.

Benign-malignant mass classification in mammogram using edge weighted local texture features

This paper introduces novel Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for the classification of mammographic masses as benign or malignant. Mass is one of the common, however, challenging evidence of breast cancer in mammography and diagnosis of masses is a difficult task. Since DRLBP and DRLTP overcome the drawbacks of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) by discriminating a brighter object against the dark background and vice-versa, in addition to the preservation of the edge information along with the texture information, several edge-preserving texture features are extracted, in this study, from DRLBP and DRLTP. Finally, a Fisher Linear Discriminant Analysis method is incorporated with discriminating features, selected by stepwise logistic regression method, for the classification of benign and malignant masses. The performance characteristics of DRLBP and DRLTP features are evaluated using a ten-fold cross-validation technique with 58 masses from the mini-MIAS database, and the best result is observed with DRLBP having an area under the receiver operating characteristic curve of 0.982.

Face detection using skin color modeling and geometric feature

The paper proposes a robust face detection technique based on skin color clustering and geometric feature. Skin regions are extracted using Gaussian skin color model in Cb-Cr space and likelihood ratio method is used to create a binary mask. Further, morphological operations and adaptive thresholding is applied to group the potential face regions and remove noisy pixels. The region thus extracted is operated by an edge filter to find the edges inside it. Finally, best ellipse searching is used to detect the frontal face outline. The skin color model was designed using a combination of two different databases, to encompass larger skin hues. Later, a total of 165 facial images from Caltech database were randomly selected to evaluate the performance of the proposed method and an accuracy of 95% was obtained.

Moving object detection using modified temporal differencing and local fuzzy thresholding

Most of the existing video object detection schemes are either computationally extensive or fail to detect moving objects in different challenging situations. In this paper, we propose a robust and computationally inexpensive scheme to detect moving objects in video. The threefold approach begins with computation of difference images using temporal information. Difference images are calculated by subtracting two input frames, at each pixel position. Instead of generating difference images using the traditional continuous frame difference approach, we propose using a fixed number of alternate frames centered around the current frame. This approach aids in reducing the computational complexity without compromising on quality of the difference images. After computation of difference images, a novel post-processing scheme is employed by utilizing gamma correction factor and Mahalanobis distance metric to reduce false positives and false negatives. Object segmentation is finally performed on the refined difference image by a local fuzzy thresholding scheme. This avoids problems that are usually encountered in hard thresholding, especially pixel misclassification, which is the most important one. For robust experimental analysis, videos from changedetction.net, CAVIAR, and http://perception.i2r datasets have been used. These selected videos contain a wide variety of common challenges faced during object detection. Some examples are the presence of dynamic backgrounds, shadows, bad weather, etc. The results establish the effectiveness of the proposed scheme over some of the existing schemes both qualitatively and quantitatively as delineated in the experimental result section.

Video error concealment through 3-D face model

All current state-of-the-art video error concealment schemes conceal the lost area through the reconstruction of 2-D patches. Reconstructed corrupted areas in the facial parts of head-and-shoulder video sequences, as in video conferencing applications, often suffer from objectionable artifacts. In this work, we present a novel video error concealment technique, which is assisted by Candide-3, a standard 3-D head-and-shoulder face model, for the reconstruction of corrupted facial regions with reduced artifact. The model is first adapted to facial images and then updated and tracked across frames, even in presence of lost macroblocks. The lost portions of the face are reconstructed through the projection of the adapted 3-D face model. The proposed concealment scheme has been experimented on sequences having facial areas such as Foreman, Carphone, News etc. and it outperforms some of the recently developed 2-D concealment schemes.

Analysis of 2D singularities for mammographic mass classification

Masses are one of the prevalent early signs of breast cancer, visible in mammogram. However, its variation in shape, size, and appearance often creates hazards in proper diagnosis of mammographic masses. This study analyses the 2D singularities of masses and their surrounding regions with Ripplet-II transform to classify them as benign and malignant. Since benign and malignant masses may change the orientation patterns of normal breast tissues differently, several textural features including Ripplet-II coefficients and statistical co-variates, derived from the Ripplet-II transformed images, are extracted to quantify the texture information of mammographic regions. The important features are then selected using stepwise logistic regression technique and evaluated using linear discriminant analysis and support vector machine with a ten-fold cross-validation. The best performance in terms of the area under the receiver operating characteristic curve of 0.91 ± 0.01 and 0.83 ± 0.01 and accuracy of 87.28 ± 0.02 and 75.60 ± 0.01 are obtained with the proposed method while experimenting with 58 images from the mini-MIAS and 200 images from the Digital Database for Screening Mammography database, respectively.

Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms

In this paper, two novel feature extraction methods, using neighborhood structural similarity (NSS), are proposed for the characterization of mammographic masses as benign or malignant. Since gray-level distribution of pixels is different in benign and malignant masses, more regular and homogeneous patterns are visible in benign masses compared to malignant masses; the proposed method exploits the similarity between neighboring regions of masses by designing two new features, namely, NSS-I and NSS-II, which capture global similarity at different scales. Complementary to these global features, uniform local binary patterns are computed to enhance the classification efficiency by combining with the proposed features. The performance of the features are evaluated using the images from the mini-mammographic image analysis society (mini-MIAS) and digital database for screening mammography (DDSM) databases, where a tenfold cross-validation technique is incorporated with Fisher linear discriminant analysis, after selecting the optimal set of features using stepwise logistic regression method. The best area under the receiver operating characteristic curve of 0.98 with an accuracy of

Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors.

94.57\%

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases

is achieved with the mini-MIAS database, while the same for the DDSM database is 0.93 with accuracy