Mass Segmentation in Automated 3-D Breast Ultrasound Using Adaptive Region Growing and Supervised Edge-Based Deformable Model.

Automated 3-D breast ultrasound has been proposed as a complementary modality to mammography for early detection of breast cancers. To facilitate the interpretation of these images, computer aided detection systems are being developed in which mass segmentation is an essential component for feature extraction and temporal comparisons. However, automated segmentation of masses is challenging because of the large variety in shape, size, and texture of these 3-D objects. In this paper, the authors aim to develop a computerized segmentation system, which uses a seed position as the only priori of the problem. A two-stage segmentation approach has been proposed incorporating shape information of training masses. At the first stage, a new adaptive region growing algorithm is used to give a rough estimation of the mass boundary. The similarity threshold of the proposed algorithm is determined using a Gaussian mixture model based on the volume and circularity of the training masses. In the second stage, a novel geometric edge-based deformable model is introduced using the result of the first stage as the initial contour. In a data set of 50 masses, including 38 malignant and 12 benign lesions, the proposed segmentation method achieved a mean Dice of 0.74 ± 0.19 which outperformed the adaptive region growing with a mean Dice of 0.65 ± 0.2 (p-value < 0.02). Moreover, the resulting mean Dice was significantly (p-value < 0.001) better than that of the distance regularized level set evolution method (0.52 ± 0.27). The supervised method presented in this paper achieved accurate mass segmentation results in terms of Dice measure. The suggested segmentation method can be utilized in two aspects: 1) to automatically measure the change in volume of breast lesions over time and 2) to extract features for a computer aided detection or diagnosis system.

Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method.

Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.

Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables.

Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.

Measuring the effects of sevoflurane on electroencephalogram using sample entropy.

BACKGROUND: Monitoring the effect of anesthetic drugs on the neural system is a major ongoing challenge for anesthetists. During the past few years, several electroencephalogram (EEG)-based methods such as the response entropy (RE) as implemented in the Datex-Ohmeda M-Entropy Module have been proposed. In this paper, sample entropy is used to quantify the predictability of EEG series, which could provide an index to show the effect of sevoflurane anesthesia. The dose-response relation of sample entropy is compared with that of RE. METHODS: EEG data from 21 subjects is collected during the induction of general anesthesia with sevoflurane. The sample entropy is applied to the EEG recording. Pharmacokinetic-pharmacodynamic modeling and prediction probability statistic are used to evaluate the efficiency of sample entropy in comparison with RE. RESULTS: Both methods track the gross changes in EEG, especially the occurrence of burst-suppression pattern at high doses of anesthetics. However, our method produces faster reaction to transients in EEG during the induction of anesthesia as indicated from the pharmacokinetic and pharmacodynamic modeled parameters and analysis around the point of loss of consciousness. Also, sample entropy correlated more closely with effect-site sevoflurane concentration than the RE. In addition, our proposed method exhibits greater resistance to noise in the EEG signals. CONCLUSION: The results demonstrate that sample entropy can estimate the sevoflurane drug effect on the EEG more effectively than the commercial RE index with a stronger noise resistance.

Using the Hilbert-Huang transform to measure the electroencephalographic effect of propofol.

Monitoring the effect of anesthetic drugs on the central nervous system is a major ongoing challenge in anesthesia research. A number of electroencephalogram (EEG)-based monitors of the anesthetic drug effect such as the bispectral (BIS) index have been proposed to analyze the EEG signal during anesthesia. However, the BIS index has received some criticism. This paper offers a method based on the Hilbert-Huang transformation to calculate an index, called the Hilbert-Huang weighted regional frequency (HHWRF), to quantify the effect of propofol on brain activity. The HHWRF and BIS indices are applied to EEG signals collected from nine patients during a controlled propofol induction and emergence scheme. The results show that both the HHWRF and BIS track the gross changes in the EEG with increasing and decreasing anesthetic drug effect (the prediction probability P(k) of 0.85 and 0.83 for HHWRF and BIS, respectively). Our new index can reflect the transition from unconsciousness to consciousness faster than the BIS, as indicated from the pharmacokinetic and pharmacodynamic modeled parameters and also from the analysis around the point of reawakening. This method could be used to design a new EEG monitoring system to estimate the propofol anesthetic drug effect.

Diabetes mellitus and sensorineural hearing loss among non-elderly people.

One ofthe known complications of diabetes is hearing impairment. This comparative study in Tehran, Islamic Republic of Iran, aimed to evaluate the association of diabetes mellitus and sensorineural hearing loss (SNHL) among a non-elderly population. Among 160 subjects aged < 60 years with no history of occupational noise exposure (80 diabetics and 80 age- and sex-matched non-diabetic controls), 45% of diabetic patients and 20% of controls had SNHL (OR 3.5, 95% CI: 1.6-6.6). Age at onset and duration of diabetes were associated with SNHL. Diabetes mellitus may be a risk factor for hearing loss regardless of age and smoking. Determining the cause of SNHL in diabetic patients may lead to development of better treatment options.

Diabetes mellitus and sensorineural hearing loss among non-elderly people

One of the known complications of diabetes is hearing impairment. This comparative study in Tehran, Islamic Republic of Iran, aimed to evaluate the association of diabetes mellitus and sensorineural hearing loss [SNHL] among a non-elderly population. Among 160 subjects aged < 60 years with no history of occupational noise exposure [80 diabetics and 80 age- and sex-matched non-diabetic controls], 45% of diabetic patients and 20% of controls had SNHL [OR 3.5, 95% CI: 1.6-6.6]. Age at onset and duration of diabetes were associated with SNHL. Diabetes mellitus may be a risk factor for hearing loss regardless of age and smoking. Determining the cause of SNHL in diabetic patients may lead to development of better treatment options