47 research outputs found
Breast Mass Segmentation Using a Semi-automatic Procedure Based on Fuzzy C-means Clustering
Mammography is the primary modality that helped in the early detection and diagnosis of women breast diseases. Further, the process of extracting the masses in mammogram represents a challenging task facing the radiologists, due to problems such as fuzzy or speculated borders, low contrast and the presence of intensity inhomogeneities. Aims to help the radiologists in the diagnosis of breast cancer, many approaches have been conducted to automatically segment the masses in mammograms. Towards this aim, in this paper, we present a new approach for extraction of tumors from region-of-interest (ROI) using the algorithm of Fuzzy C-Means (FCM) setting two clusters for semi-automated segmentation. The proposed method meant to select as input data the set of pixels that enable to get the meaningful information required to segment the masses with high accuracy. This could be accomplished through eliminating unnecessary pixels, which influence on this process through separating it outside of the input data using an optimal threshold given by monitoring the change of clusters rate during the process of threshold decrementing. The proposed methodology has successfully segmented the masses, with an average sensitivity of 82.02% and specificity of 98.23%
Discriminative Localized Sparse Representations for Breast Cancer Screening
Breast cancer is the most common cancer among women both in developed and
developing countries. Early detection and diagnosis of breast cancer may reduce
its mortality and improve the quality of life. Computer-aided detection (CADx)
and computer-aided diagnosis (CAD) techniques have shown promise for reducing
the burden of human expert reading and improve the accuracy and reproducibility
of results. Sparse analysis techniques have produced relevant results for
representing and recognizing imaging patterns. In this work we propose a method
for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA).
In this work we apply dictionary learning to our block based sparse analysis
method to classify breast lesions as benign or malignant. The performance of
our method in conjunction with LC-KSVD dictionary learning is evaluated using
10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results
indicate that the proposed sparse analyses may be a useful component for breast
cancer screening applications
Compliance of publicly available mammographic databases with established case selection and annotation requirements
Mammographic databases play an important role in the development of algoritms aiming to improve Computer-Aided Detection and Diagnosis systems (CAD). However, these often do not take into consideration all the requirements needed for a proper study, previously discussed at the Biomedical Image Processin Meeting in 1993.info:eu-repo/semantics/publishedVersio
Computer-Aided Diagnosis of Mammographic Masses Detection Of Ascendable Images Features
In any case, the vast majority of them miss the mark concerning adaptability in the recovery arrange, and their analytic precision is, accordingly, restricted. To beat this disadvantage, we propose a versatile technique for recovery and conclusion of mammographic masses specifically, for an inquiry mammographic zone of interest (ROI), scale-in variation include transform(SIFT)features are removed and sought in a vocabulary tree, which stores all the quantized highlights of already analysed mammographic ROIs. Furthermore, to completely apply the discriminative energy of SIFT highlights, logical data in the vocabulary tree is utilized to refine the weights of tree hubs
Evaluating the Effectiveness of 2D and 3D Features for Predicting Tumor Response to Chemotherapy
2D and 3D tumor features are widely used in a variety of medical image
analysis tasks. However, for chemotherapy response prediction, the
effectiveness between different kinds of 2D and 3D features are not
comprehensively assessed, especially in ovarian cancer-related applications.
This investigation aims to accomplish such a comprehensive evaluation. For this
purpose, CT images were collected retrospectively from 188 advanced-stage
ovarian cancer patients. All the metastatic tumors that occurred in each
patient were segmented and then processed by a set of six filters. Next, three
categories of features, namely geometric, density, and texture features, were
calculated from both the filtered results and the original segmented tumors,
generating a total of 1595 and 1403 features for the 3D and 2D tumors,
respectively. In addition to the conventional single-slice 2D and full-volume
3D tumor features, we also computed the incomplete-3D tumor features, which
were achieved by sequentially adding one individual CT slice and calculating
the corresponding features. Support vector machine (SVM) based prediction
models were developed and optimized for each feature set. 5-fold
cross-validation was used to assess the performance of each individual model.
The results show that the 2D feature-based model achieved an AUC (area under
the ROC curve [receiver operating characteristic]) of 0.84+-0.02. When adding
more slices, the AUC first increased to reach the maximum and then gradually
decreased to 0.86+-0.02. The maximum AUC was yielded when adding two adjacent
slices, with a value of 0.91+-0.01. This initial result provides meaningful
information for optimizing machine learning-based decision-making support tools
in the future