Mass Detection Method Research Articles

Mueller matrix imaging of electrospun ultrafine fibers for morphology detection.

In the fields of tissue engineering, drug delivery, and filtering material, electrospun ultrafine fibers with micropores have important applications. A scanning electron microscope (SEM) is typically used to observe the microstructure of ultrafine fibers, but SEM has disadvantages, such as small field of vision, high cost, and ionization damage. Given this, 16 (4×4) full transmission Mueller matrices of electrospun fibers with three different morphologies, i.e., smooth surface, microporous, and beaded microspheres, were obtained. We find that the Mueller matrix element M44 is most sensitive to the micropores on fibers. In the M44 transformed image proposed in this paper, the different morphologies and contours of fibers can be clearly shown, providing a low-cost method for mass, nondestructive, and rapid morphology detection of fibers.

Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features

A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.

A CNN based method for automatic mass detection and classification in mammograms

A novel system for detection and classification of masses in breast mammography is introduced. The system integrates a breast segmentation module together with a modified region-based convolutional network to obtain detection and classification of masses according to BI-RADS score. While most of the previous work on mass identification in breast mammography has focused on classification, this study proposes to solve both the detection and the classification problems. The method is evaluated on a large multi-centre clinical data-set and compared to ground truth annotated by expert radiologists. Preliminary experimental results show the high accuracy and efficiency obtained by the suggested network structure. As the volume and complexity of data in health care continues to accelerate generalising such an approach may have a profound impact on patient care in many applications.

A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms

Background and ObjectiveEarly detection and diagnosis of breast cancer through mammography screening reduces breast cancer mortality by around 20%. However it is often a complex process to differentiate abnormalities due to the ill-defined margins and subtle appearances. MethodThis paper investigates a new computer aided approach to detect the abnormalities in the digital mammograms using a Dual Stage Adaptive Thresholding (DuSAT). The suspicious mass region is identified using global histogram and local window thresholding method. The global thresholding is done based on the Histogram Peak Analysis (HPA) of the entire image and the threshold is obtained by maximizing the proposed threshold selection criteria. The local thresholding is carried out for each pixel in a defined neighborhood window that provides precise segmentation results. ResultsThe algorithm is verified with 300 images in the DDSM database and 170 images in the mini-MIAS database. Experimental results show that the proposed algorithm achieves an average sensitivity of 92.5% with 1.06 FP/image for DDSM database and an average sensitivity of 93.5% with 0.62 FP/image for mini-MIAS database. ConclusionThe achieved results depict that the proposed approach provides better results compared to other state-of-art methods for mass detection that helps the radiologists in diagnosis of breast cancer at early stage.

Toward Higher-Order Mass Detection: Influence of an Adsorbate's Rotational Inertia and Eccentricity on the Resonant Response of a Bernoulli-Euler Cantilever Beam.

In this paper a new theoretical model is derived, the results of which permit a detailed examination of how the resonant characteristics of a cantilever are influenced by a particle (adsorbate) attached at an arbitrary position along the beam’s length. Unlike most previous work, the particle need not be small in mass or dimension relative to the beam, and the adsorbate’s geometric characteristics are incorporated into the model via its rotational inertia and eccentricity relative to the beam axis. For the special case in which the adsorbate’s (translational) mass is indeed small, an analytical solution is obtained for the particle-induced resonant frequency shift of an arbitrary flexural mode, including the effects of rotational inertia and eccentricity. This solution is shown to possess the exact first-order behavior in the normalized particle mass and represents a generalization of analytical solutions derived by others in earlier studies. The results suggest the potential for “higher-order” nanobeam-based mass detection methods by which the multi-mode frequency response reflects not only the adsorbate’s mass but also important geometric data related to its size, shape, or orientation (i.e., the mass distribution), thus resulting in more highly discriminatory techniques for discrete-mass sensing.

A novel computer aided breast mass detection scheme based on morphological enhancement and SLIC superpixel segmentation

To develop a computer-aided detection (CAD) scheme for mass detection on digitized mammograms that achieves a high sensitivity while maintaining a low false positive (FP) rate using morphological enhancement and simple linear iterative clustering (SLIC) method. The authors developed a multiple stage method for breast mass detection. The proposed CAD scheme consists of five major components: (1) preprocessing based on morphological enhancement, which enhances mass-like patterns while removing unrelated background clutters, (2) segmentation of mass candidates based on the SLIC method, which groups mass and background tissue into different regions, (3) prescreening of suspicious regions using rule-based classification that eliminates regions unlikely to represent masses, (4) potential lesion contour refinement based on distance regularized level set evolution, and (5) FP reduction based on feature extraction and an ensemble of undersampled support vector machines. Two datasets were built to design and evaluate the system: a mass dataset containing 187 cases (386 mammograms) and a nonmass dataset containing 88 mammograms. All cases were acquired from the digital database for screening mammography (DDSM). Approximately two thirds of the available masses were used for training the system, and the remaining masses and nonmass dataset were used for testing. Testing of the proposed CAD system on the mass dataset yielded a mass-based sensitivity of 98.55%, 97.10%, 92.75% at 0.84, 0.63, 0.55 FP mark/image, respectively. Tested on the nonmass dataset, the scheme showed a FP rate of 0.55, 0.34, 0.30 mark/image. The results indicate that the system is promising in improving the performance of current CAD systems by reducing FP rate while achieving relatively high sensitivity.

Towards cost reduction of breast cancer diagnosis using mammography texture analysis

In this paper we analyse the performance of various texture analysis methods for the purpose of reducing the number of false positives in breast cancer detection; as a result, the cost of breast cancer diagnosis would be reduced. We consider well-known methods such as local binary patterns, histogram of oriented gradients, co-occurrence matrix features and Gabor filters. Moreover, we propose the use of local directional number patterns as a new feature extraction method for breast mass detection. For each method, different classifiers are trained on the extracted features to predict the class of unknown instances. In order to improve the mass detection capability of each individual method, we use feature combination techniques and classifier majority voting. Some experiments were performed on the images obtained from a public breast cancer database, achieving promising levels of sensitivity and specificity.

Mode-shape-based mass detection scheme using mechanically diverse, indirectly coupled microresonator arrays

We explore vibration localization in arrays of microresonators used for ultrasensitive mass detection and describe an algorithm for identifying the location and amount of added mass using measurements of a vibration mode of the system. For a set of sensing elements coupled through a common shuttle mass, the inter-element coupling is shown to be proportional to the ratio of the element masses to the shuttle mass and to vary with the frequency mistuning between any two sensing elements. When any two elements have sufficiently similar frequencies, mass adsorption on one element can result in measurable changes to multiple modes of the system. We describe the effects on system frequencies and mode shapes due to added mass, in terms of mass ratio and frequency spacing. In cases in which modes are not fully localized, frequency-shift-based mass detection methods may give ambiguous results. The mode-shape-based detection algorithm presented uses a single measured mode shape and corresponding natural frequency to identify the location and amount of added mass. Mass detection in the presence of measurement noise is numerically simulated using a ten element sensor array. The accuracy of the detection scheme is shown to depend on the amplitude with which each element vibrates in the chosen mode.

A new automatic mass detection method for breast cancer with false positive reduction

Mass localization is a crucial problem in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, a new automatic mass detection method for breast cancer in mammographic images is proposed. Firstly, suspicious regions are located with an adaptive region growing method, named multiple concentric layers (MCL) approach. Prior knowledge is utilized by tuning parameters with training data set during the MCL step. Then, the initial regions are further refined with narrow band based active contour (NBAC), which can improve the segmentation accuracy of masses. Texture features and geometry features are extracted from the regions of interest (ROI) containing the segmented suspicious regions and the boundaries of the segmentation. The texture features are computed from gray level co-occurrence matrix (GLCM) and completed local binary pattern (CLBP). Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist׳s diagnosis. To deal with the imbalance problem regarding the number of non-masses and masses, supersampling and downsampling are incorporated. The method was evaluated on a dataset with 429 craniocaudal (CC) view images, containing 504 masses. Among them, 219 images containing 260 masses are used to optimize the parameters during MCL step, and are used to train SVM. The remaining 210 images (with 244 masses) are used to test the performance. Masses are detected with 82.4% sensitivity with 5.3 false positives per image (FPsI) with MCL, and after active contour refinement, feature analysis and classification, it obtained 1.48 FPsI at the sensitivity 78.2%. Testing on 164 normal mammographic images showed 5.18 FPsI with MCL and 1.51 FPsI after classification. Experiments on mediolateral oblique (MLO) images have also been performed, the proposed method achieved a sensitivity 75.6% at 1.38 FPsI. The method is also analyzed with free response operating characteristic (FROC) and compared with previous methods. Overall, the proposed method is a promising approach to achieve low FPsI while maintaining a high sensitivity.

Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM.

Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; this approach has aided in the detection and diagnosis of cancer. The present work proposes a method for the automatic detection of masses in digital mammograms by using quality threshold (QT), a correlogram function, and the support vector machine (SVM). This methodology comprises the following steps: The first step is to perform preprocessing with a low-pass filter, which increases the scale of the contrast, and the next step is to use an enhancement to the wavelet transform with a linear function. After the preprocessing is segmentation using QT; then, we perform post-processing, which involves the selection of the best mass candidates. This step is performed by analyzing the shape descriptors through the SVM. For the stage that involves the extraction of texture features, we used Haralick descriptors and a correlogram function. In the classification stage, the SVM was again used for training, validation, and final test. The results were as follows: sensitivity 92.31 %, specificity 82.2 %, accuracy 83.53 %, mean rate of false positives per image 1.12, and area under the receiver operating characteristic (ROC) curve 0.8033. Breast cancer is notable for presenting the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis means a considerable increase in the survival chance of the patients. The methodology proposed herein contributes to the early diagnosis and survival rate and, thus, proves to be a useful tool for specialists who attempt to anticipate the detection of masses.

A PYRAMID-BASED MASS DETECTION SCHEME BASED ON TEXTURE ANALYSIS AND NEURAL CLASSIFIER FOR DIGITAL MAMMOGRAMS

In the paper, we proposed a pyramid-based mass detection method based on texture analysis and neural classifier for digital mammograms. The proposed mass detection method is composed of four parts: pyramid decomposition, region of interest (ROI) selection, feature extraction and neural classifier. Based on pyramid decomposition, a coarse-to-fine approach was utilized to achieve mass detection for reducing computational complexity in the proposed scheme. For decreasing computational complexity, ROI selection where a thresholding algorithm and polynomial function fitting were to find the breast area is also exploited to remove nonbreast regions in the proposed scheme. In the texture analysis, the intensity and texture information extracted from spatial and wavelet domains are utilized to analyze each pixel within the ROI. After feature extraction, these extracted texture features are combined with a supervised neural network to detect masses in the ROI. To evaluate the performance of the proposed scheme, the mammograms of 19 patients captured in Taiwan are used for testing. The experimental result shows that ROI selection can localize breast regions well for further analysis. In addition, the average recall rate of our proposed scheme is more than 86%. Therefore, these experimental results demonstrate that the proposed pyramid-based scheme can achieve mass detection.

Simultaneous quantitation of nucleosides, nucleobases, amino acids, and alkaloids in mulberry leaf by ultra high performance liquid chromatography with triple quadrupole tandem mass spectrometry

An ultra high performance liquid chromatography coupled with a triple quadrupole mass detection and electrospray ionization mass spectrometry method has been established for the simultaneous determination of the 14 nucleosides and nucleobases, 24 amino acids and two main alkaloids in mulberry leaf. In this method, a complicated mobile phase, the flow rate of which was 0.4 mL/min, was applied to the gradient elution, which provided a satisfied separation of the 40 compounds. The present method was validated, and sufficient reproducibility and accuracy were obtained for the quantitative measurement of the 40 compounds. The method was subsequently applied to ten mulberry leaves and the results showed that almost all of these samples were rich in nucleosides, nucleobases, amino acids, and alkaloids. The proposed method, which is convenient and economical, could serve as a prerequisite for the quality control of mulberry leaf herbs and be applied analogously to other Chinese medicines.

An efficient fractal method for detection and diagnosis of breast masses in mammograms.

In this paper, we present an efficient fractal method for detection and diagnosis of mass lesion in mammogram which is one of the abnormalities in mammographic images. We used 110 images that were carefully selected by a radiologist, and their abnormalities were also confirmed by biopsy. These images included circumscribed benign, ill-defined, and spiculated malignant masses. Firstly, we discriminated lesions automatically using new fractal dimensions. The results which were examined by different types of breast density showed that the proposed method was able to yield quite satisfactory detection results. Secondly, noting that contours of masses playing the most important role in diagnosis of different mass types, we defined new fractal features based on information extraction from the contours. This information is able to identify the roughness in mass contours and determines the extent of spiculation or smoothness of the masses. In this manner, in classification of the spiculated malignant masses from the circumscribed benign tumors, we achieved highly satisfactory results, i.e., 0.98 measured in terms of area under ROC curve (AUC). In this paper, it is also shown that the roughness in contours is a suitable characteristic feature for diagnosis of ill-defined malignant tumors with AUC equal to 0.94 in their classification. The extracted information was also found to be useful in the classification of early malignancies whereas in the classification of spiculated and ill-defined malignant masses in their early stage from those of benign tumors, we achieved high accuracy of 0.99 and 0.90 for AUC, respectively.

Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm

In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection – CADe) and diagnosis (Computer-Aided Diagnosis – CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean±standard deviation value of AOM for the proposed method was 79.2±8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views.

Ultra-sensitive analysis of a cantilevered single-walled carbon nanocone-based mass detector

The ultra-sensitivity of mass detectors using individual cantilevered single-walled carbon nanocone (SWCNC) resonators is first investigated. A higher-order gradient theory, derived at the atomic level, is applied for modeling SWCNC resonators. Numerical simulations using a mesh-free computational framework based on moving Kriging interpolation are conducted to investigate the mass sensitivity of cantilevered SWCNC resonators with extra mass loading as well as with equivalent single-walled carbon nanotube (SWCNT) resonators. Comparison of the magnitude of resonant frequency shifts, the key criterion for mass sensitivity, of these two kinds of resonators demonstrates a far higher mass sensitivity for SWCNC resonators than for SWCNT resonators, thus suggesting a new method for ultra-sensitive mass detection via SWCNC resonators. The dependence of the mass sensitivity of SWCNC resonators on height and top radii has been examined. A reduction in the height of SWCNC resonators gives rise to a considerable increase in mass sensitivity. The mass sensitivity of a 6 nm high SWCNC resonator can even reach a level of 10−22 g. It is noteworthy that the top radii of SWCNC resonators have a slight effect on frequency shifts. Another interesting observed phenomenon is that a deviation in the height of 19.2° SWCNC resonators leads to little loss in precision of mass detection when the attached mass is smaller than 10−20 g. This superior characteristic indicates that SWCNC-based mass detectors have great potential in practical applications.

Assessment of a novel mass detection algorithm in mammograms

Mammography is the most effective procedure for an early detection of the breast abnormalities. Masses are a type of abnormality, which are very difficult to be visually detected on mammograms. In this paper an efficient method for detection of masses in mammograms is implemented. The proposed mass detector consists of two major steps. In the first step, several suspicious regions are extracted from the mammograms using an adaptive thresholding technique. In the second step, false positives originating by the previous stage are reduced by a machine learning approach. All modules of the mass detector were assessed on mini-MIAS database. In addition, the algorithm was tested on INBreast database for more validation. According to FROC analysis, our mass detection algorithm outperforms other competing methods. We should not just insist on sensitivity in the segmentation phase because if we forgot FP rate, and our goal was just higher sensitivity, then the learning algorithm would be biased more toward false positives and the sensitivity would decrease dramatically in the false positive reduction phase. Therefore, we should consider the mass detection problem as a cost sensitive problem because misclassification costs are not the same in this type of problems.

Noise sensitivity of a mass detection method using vibration modes of coupled microcantilever arrays

Numerical simulation is used to explore the sensitivity to measurement noise of a mass detection approach that uses eigenmodes of an array of nominally identical micro- or nanomechanical resonators. The mode shapes are perturbed to simulate measurement noise, and resulting errors in identifying variations in mass are quantified as a function of array size, coupling strength, and level of mass variation. Sensitivity to measurement noise is low for lightly coupled arrays of nearly identical elements and increases when mass variation causes significant mode localization. For any mass variation level, an optimal combination of array size and coupling strength minimizes noise sensitivity.

Analysis of Tumor Characteristics based on MCA Decomposition and Watershed Segmentation

An accurate and standardized technique for breast tumor segmentation is a critical step for monitoring and quantifying breast cancer. The fully automated tumor segmentation in mammograms presents many challenges related to characteristics of an image. In this paper, two different methods for mass detection are applied. First method uses morphological component analysis and multiple layer thresholding. Second method uses watershed segmentation. Features are extracted and the best one is found out for efficient identification of breast cancer.

Clomipramine concentration and withdrawal symptoms in 10 neonates

After in utero exposure to tricyclic antidepressants, neonatal withdrawal symptoms have been reported with an estimated incidence between 20 and 50%; however, few data are available for clomipramine. This could also be the case for neonatal pharmacokinetic clomipramine parameters and so this study was set up. Babies exposed to clomipramine in utero were included in an observational study, approved by the local ethics committee, after written informed consent. Withdrawal symptoms were scored at 12, 24 and 48 h after birth using the Finnegan score. Plasma concentrations were determined using an in-house-developed, validated liquid chromatography with mass detection (LC-MSMS) method at 0, 12, 24 and 48 h after birth. We found that three of 11 pregnancies were complicated with pre-eclampsia. Ten neonates were observed for clomipramine withdrawal symptoms. The observed withdrawal symptoms were too short a period of sleep after feeding (6), poor feeding (3), mild to severe tremors (6), hyperactive Moro reflex (3) and respiratory rate >60 breaths min(-1). Serious withdrawal reactions, such as tachycardia and cyanosis, were seen. We calculated a half-life value of 42 ± 16 h for clomipramine in neonates. Only a weak correlation was found between withdrawal reactions and clomipramine plasma concentration or desmethylclomipramine plasma concentration. In neonates, clomipramine is eliminated with a half-life value of 42 h, compared with 20 h in adults. In two of 10 neonates, tachycardia and cyanosis were seen as serious withdrawal symptoms after maternal use of clomipramine.

Masses Detection in Digital Mammogram by Gray Level Reduction using Texture coding Method

Breast cancer is the most common cancer in women around the world. Various countries including the UAE offer asymptomatic screening for the disease. The interpretation of mammograms is a very challenges task and is subject to human error. Computeraided detection and diagnosis have been proposed as a second reader for helping radiologists perform this difficult task. Texture features have been widely used as classification of masses in digital mammogram. In this paper we proposed a method for automatic detection of masses in digital mammogram. The proposed method uses the coding technique achieved good accuracy with Linear Discriminant Analysis (LDA) classification. The classification accuracy by using the coded images is improved much compared to one that obtained from the original image.