Classifying breast masses in volumetric whole breast ultrasound data: a 2.5-dimensional approach

The aim of this paper is to investigate a 2.5-dimensional approach in classifying masses as benign or malignant in volumetric anisotropic voxel whole breast ultrasound data. In this paper, the term 2.5-dimensional refers to the use of a series of 2-dimensional images. While mammography is very effective in breast cancer screening in general, it is less sensitive in detecting breast cancer in younger women or women with dense breasts. Breast ultrasonography does not have the same limitation and is a valuable adjunct in breast cancer detection. The current study focuses on a new 2.5-dimensional approach in analyzing the volumetric whole breast ultrasound data for mass classification

Computer-aided Diagnosis of Breast Elastography

Ultrasonography has been an important imaging technique for detecting breast tumors. As opposed tothe conventional B-mode image, the real-time tissue elastography by ultrasound is a new technique for imagingthe elasticity and applied to detect the stiffness of tissues. The red region of color elastography indicatesthe soft tissue and the blue one indicates the hard tissue. The harder tissue usually is classified as malignancy.In this paper, the authors proposed a computer-aided diagnosis( CAD) system on elastography tomeasure whether this system is effective and accurate to classify the tumor into benign and malignant. Accordingto the features of elasticity, the color elastography was transferred to hue, saturation, and value(HSV) color space and extracted meaningful features from hue images. Then the neural network was utilizedin multiple features to distinguish tumors. In this experiment, there are 180 pathology-proven cases including113 benign and 67 malignant cases used to examine the classification. The results of the proposedsystem showed an accuracy of 83.89 %, a sensitivity of 82.09 % and a specificity of 84.96 %. Compared withthe physician\u27s diagnosis, an accuracy of 78.33 %, a sensitivity of 53.73 % and a specificity of 92.92 %, theproposed CAD system had better performance. Moreover, the agreement of the proposed CAD system andthe physician\u27s diagnosis was calculated by kappa statistics, the kappa 0.64 indicated there is a fair agreementof observers

パーキンソン カンレン シッカン ニオケル ケイズガイ チョウオンパ ケンサ ニヨル チュウノウ コクシツ ノ コウキド ヘンカ ノ ケントウ

目 的:パーキンソン病 (Parkinson disease:PD),多系統萎縮症 (multiple system atrophy:MSA),進行性核上性麻痺 (progressive supranuclear palsy:PSP) の患者において経頭蓋超音波検査 (transcranial sonography:TCS) よる中脳黒質の高輝度変化を検討した.方 法:パーキンソン関連疾患連続110 例 (PD 86 例,MSA 12 例,PSP 12 例) と健常者34 例に対しTCSを施行した.中脳黒質を観察しえたPD 47 例,MSA 10 例,PSP 6 例,健常者32 例を解析対象として中脳黒質高輝度所見を評価した.定性評価は高輝度の程度によって視察的にI:none or faint,II:equivocal,III:definite,IV:marked の4 段階に分類した.定量評価は中脳黒質で高輝度変化の面積が0.20 cm2 以上のとき,病的な黒質高輝度変化と定義した.結 果:定性評価では,高輝度範囲が視察的に病的と判定されるIII+IVの割合は,PD 72.4%,MSA 10.0%,PSP 66.7%,健常者3.1%であった.定量評価では,PD 63.8%,MSA 20.0%,PSP 66.7%,健常者9.4%で病的な高輝度変化をみとめた.PD,PSP で病的な高輝度変化の割合が多かった.PSP をPSP-parkinsonism( PSPP)とRichardson\u27s syndrome の2 群に分けた場合,前者では病的な高輝度変化を3 例中3 例 (100%), 後者では3 例中1 例( 33.3%) に認められ,PSP-P で割合が高かった.MSA では10 例中2 例( 20%) に病的な高輝度を認め,いずれもパーキンソン病型の多系統萎縮症であった.結 論:パーキンソン関連疾患における病的な中脳黒質高輝度変化は,疾患特異性というよりも,パーキンソニズムの症候と関連し,ドパミン神経細胞の脆弱性を示す所見と推察された.Objective:We investigated substantia nigra (SN) hyperechogenicitydetermined by transcranial sonography(TCS) to detect abnormalities, and compare findings withthose from Parkinson disease (PD), multiple system atrophy(MSA), progressive supranuclear palsy (PSP) or controlsubjects.Method:In this study, echogenicity of SN was examinedin consecutive 110 parkisonian disorders patients with PD86, MSA12, PSP 11, and 34 control subjects. A sufficientbone window for TCS was available in 47 of 86( 71.2%) inthe PD group, 10 of 12( 86.3 %) in the MSA group, 6 of 11(54.5%) in the PSP group and 32 of 34( 94.1%) in the controlgroup. SN hyperechogenicity was scored using a fourpointscale as follows:I=none or faint, II=equivocal, III=definite, IV=marked. In accordance with previously reportedcut-off values, areas of echogenicity £ 0.19 cm2 wereclassified as normal and areas of echogenicity £ 0.20 cm2were classified as pathological SN hyperechogenicity.Results:The frequency of SN hyperechogenicity, assessedas III and IV scales, was significantly increased in PDpatients, and observed in 72 . 4 % of assessable SN(34/47);qui-squire;p=0.001, vs. controls). The meansize of the SN hyperechogenic area in the PD group, MSAgroup and PSP group was 0.26 cm2±0.13, 0.11 cm2±0.11and 0.23 cm2±0.04, respectively, compared with 0.07 cm2±0.06 in the control group.We have identified two clinical phenotypes, such as Richardson\u27ssyndrome (RS) and PSP-parkinsonism (PSP-P).All of three PSP-P (100%) patients showed a pathologicalSN hyperechogenicity.Conclusion:SN hyperechogenicity was associated with asymptom of parkinsonism rather than disease specificity,and suggested a vulnerability marker of the dopaminergicneuron

Novel synthesis process and structure refinements of Li{sub 4}Mn{sub 5}O{sub 12} for rechargeable lithium batteries

Well crystallized Li4Mn5O12 was prepared from LiOAc-Mn(NO3)2 under flowing oxygen. Rietveld refinement with XRD and neutron powder diffraction indicated that Li4Mn5O12 has cubic spinel structure in which the Li ions occupy both the tetrahedral sites 8a and part of the octahedral sites 16d but not the 16c sites, while all the Mn ions occupy the 16d sites of the space group Fd{bar 3}m. The lattice parameter was found to be sensitive to synthesis temperature owing to variation in Mn valence. Sample prepared at 500 C showed better electrode performance: a rechargeable capacity of 135 mAh/g for the cell Li/Li4Mn5O12 at cell voltages 2.5-3.6 V. It is found that Mn oxidation state in Li4Mn5O12 has a strong effect on electrode performance of Li/Li4Mn5O12 cell

Structure and electrochemical characterization of L11+xMn2-xO4 spinels for rechargeable lithium batteries

Peer reviewed: YesNRC publication: Ye

ANALYSIS OF ELASTOGRAPHIC AND B-MODE FEATURES AT SONOELASTOGRAPHY FOR BREAST TUMOR CLASSIFICATION

The purpose of this study was to evaluate the accuracy of neural network analysis of elastographic features at sonoelastography for the classification of biopsy-proved benign and malignant breast tumors. Sonoelastography of 181 solid breast masses (113 benign and 68 malignant tumors) was performed for 181 patients (mean age, 47 years; range, 24-75 years). After the manual segmentation of the tumors, five elastographic features (strain difference, strain ratio, mean, median and mode) and six B-mode features (orientation, undulation, angularity, average gradient, gradient variance and intensity variance) were computed. A neural network was used to classify tumors by the use of these features. The Student`s t test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. Area under ROC curve (Az) values of the three elastographic features mean (0.87), median (0.86) and mode (0.83)-were significantly higher than the Az values for the six B-mode features (0.54-0.69) (p < 0.01). Accuracy, sensitivity, specificity and Az of the neural network for the classification of solid breast tumors were 86.2% (156/181), 83.8% (57/68), 87.6% (99/113) and 0.84 for the elastographic features, respectively, and 82.3% (149/181), 70.6% (48/68), 89.4% (101/113) and 0.78 for the B-mode features, respectively, and 90.6% (164/181), 95.6% (65/68), 87.6% (99/113) and 0.92 for the combination of the elastographic and B-mode features, respectively. We conclude that sonoelastographic images and neural network analysis of features has the potential to increase the accuracy of the use of ultrasound for the classification of benign and malignant breast tumors. (E-mail: [email protected]) (C) 2009 World Federation for Ultrasound in Medicine & Biology.This work was supported by a grant from the National Science Council of the Republic of China (NSC96-2221- E-002-268-MY3) and a grant of the Korea Healthcare technology R and D Project, Ministry for Health, Welfare and Family Affairs, Republic of Korea (A070001).Cho N, 2009, EUR RADIOL, V19, P1621, DOI 10.1007/s00330-009-1335-4Muller M, 2009, ULTRASOUND MED BIOL, V35, P219, DOI 10.1016/j.ultrasmedbio.2008.08.018Scaperrotta G, 2008, EUR RADIOL, V18, P2381, DOI 10.1007/s00330-008-1032-8Zhi H, 2008, ACAD RADIOL, V15, P1347, DOI 10.1016/j.acra.2008.08.003Tanter M, 2008, ULTRASOUND MED BIOL, V34, P1373, DOI 10.1016/j.ultrasmedbio.2008.02.002Cho N, 2008, KOREAN J RADIOL, V9, P111, DOI 10.3348/kjr.2008.9.2.111GONZALEZ RC, 2008, DIGITAL IMAGE PROCESBooi RC, 2008, ULTRASOUND MED BIOL, V34, P12, DOI 10.1016/j.ultrasmedbio.2007.07.003Burnside ES, 2007, RADIOLOGY, V245, P401, DOI 10.1148/radiol.2452061805Shen WC, 2007, ULTRASOUND MED BIOL, V33, P1688, DOI 10.1016/j.ultrasmedbio.2007.05.016Shen WC, 2007, ACAD RADIOL, V14, P928, DOI 10.1016/j.acra.2007.04.016Sahiner B, 2007, RADIOLOGY, V242, P716, DOI 10.1148/radiol.2423051464Itoh A, 2006, RADIOLOGY, V239, P341, DOI 10.1148/radiol.2391041676Regner DM, 2006, RADIOLOGY, V238, P425, DOI 10.1148/radiol.2381041336SVENSSON WE, 2006, ULTRASOUND MED BI S1, V32, P173Moon WK, 2005, RADIOLOGY, V236, P458, DOI 10.1148/radiol.2362041095Hong AS, 2005, AM J ROENTGENOL, V184, P1260SVENSSON WE, 2005, P 4 INT C ULTR MEAS, P87Joo S, 2004, IEEE T MED IMAGING, V23, P1292, DOI 10.1109/TMI.2004.834617Horsch K, 2004, ACAD RADIOL, V11, P272, DOI 10.1016/S1076-6332(03)00719-0Chen CM, 2003, RADIOLOGY, V226, P504, DOI 10.1148/radiol.2262011843*AM COLL RAD, 2003, BREAST IM REP DAT SYHall TJ, 2003, ULTRASOUND MED BIOL, V29, P427, DOI 10.1016/S0301-5629(02)00733-0SHAPIRO LG, 2001, COMPUTER VISIONHAYKIN S, 1999, NEURAL NETWORKS COMPGarra BS, 1997, RADIOLOGY, V202, P79OPHIR J, 1991, ULTRASONIC IMAGING, V13, P111