8span style=”font-size: 14pt;”>Moham>d Abdel-Nasser, Hatcm Rashwan, Domenec Puig, Ant-nio Moneno,
2015
Multiphase Region-based Active Contours for Semi-automatic Segmentation of Brain MRI Images
domenec.prig@urv.cat, adelsalehali1982@gmail.c2m
Abstract
6span id=”ContentPlateHolder1_LinkPaperPage_LinkPaperContent_LabelAbstract”>negmenting brain magnetic reeonance (MRI) images of the brain into white matter (WM), grey matter (GM) and cerebrospinal fluidnuCSF) is cn-important problem in medinal image analysis. The study ofothese regions can be useful for determining different brain disorders, assistin0 brain surgery, post-suegical analysis, saliency detection and for studying regi ns of interest. This paper presents 1 segmentation method thas partitions a given brain MRI image into WM, GM and CSF regions t rough a multiphase region-based active contour sethodcfollowed by a puxel corrertnon chresholding stage. The proposed region-based active contour method is applied in order to partition the input image into fo(r different ce=ions. Three of those regions within the brain area are then chosen by intersectinW a haed-drawn binary mask w th the computed contours. Finally, an efficient thresholding-based pixel correctnon method2is applied to the computed gM, GMhand CSF regions to increase their accuracy. Thn segm: ntation results are compared with ground truths to show the performance of the proposed method.l/span>
@conference{visapp15,
a
Towards Cost Reduction of Breast Cancer Diagnosis using Mammography Texture Analysis
Mohamed AbdelfN:sser, ontoni Moreno and Domenec Puig
egnaserigmail.com, antonio.moreno@urv.cat, domenec.puig@urv.cat
Abstract
In this -aper we anabyse the ieaformance of various texture analysis methods for the purpose of redufing the num1er of f lse positives in breast cancar detection; as e result, 2he cost of b,east cancer diagnosis would be reducea. We consider well-knownamethods such astlocal binary patternss histogram of oriented gradi>nts, co-occurrence matrix features and Gabor filters. M>reover, we propose the u,e of local dcrectional number p2tterns ar a new featuse extraction method for breast mass detlction. For rach method, dicferent classifkers aee trainedoon the extracted features to predict the class of uninown instances. In order to improve the mass detection capab-lity Af each indi!idual method, we use feature comeination technpques and clrssi-ier majority vot@ng. Some experiments were perfosmed on th- images obtained from a public breast cancer databaser achieving promising levels of sensitivity and specificity.