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ABSTRACT The aim of this paper is to show a technique to speed up the interpretation of bone scans in order to determine the presence of early bone metastasis. This is done using the gray levels histogram of the region of interest. The technique is intended to assist in the bone scans interpretation in order to provide a successful diagnosis. During the analysis, three types of histograms were observed on the regions of interest. If the histogram is narrow and shifted toward the origin, the bone scan is free of metastasis. If it is shifted to the right and slightly broadened, indicates the presence of a bone anomaly different from a metastasis. On the other hand, if the histogram is more broadened and shifted to the right, is suggests the presence of metastasis. This histogram is characterized by displaying small curls on the right side providing information about the metastatic disease stage, which could be low-amplitude peaks and have a short length, if the metastasis is in early stage, or high-amplitude peaks and a long length, if is advanced. Finally, the analyzed region is displayed in false color considering the minimum gray levels observed in the histogram.
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Objective To identify species of common sarcosaprophagous flies based on digital image analysis of veins, in order to provide new idea for fast and accurate species identification of sarcosaprophagous flies in forensic entomology. Methods Random trapping of 226 male and female sarcosaprophagous flies that comprised of 7 common species, including Sarcophaga peregrina, Parasarcophaga ruficornis, Sarcophaga dux, Seniorwhitea reciproca, Bercaea cruentata, Aldrichina grahami, and Synthesiomysia nudiseta with carrion in the field was conducted. The 17 landmarks on the right wing of each fly were digitally processed and the images were analyzed. The effects of allometry were evaluated using a permutation test. Wing shape variations among 7 sarcosaprophagous fly species and female species was analyzed using canonical variate analysis （CVA）. Additionally, cross-validation test was used to evaluate the reliability of classification. Results Among 7 sarcosaprophagous fly species and female species, the effect of allometry had statistical significance （P<0.05）. The CVA results showed that among 7 sarcosaprophagous fly species and female species, differences in the wing shape were significant, and the first two canonical variates accounted for 82.9% and 84.1% of the total variation of vein shape. Vein digital image analysis can be used to separate the 7 common sarcosaprophagous flies, with an overall species identification accuracy of 81.2%-100.0%, and with a species identification accuracy of 75.0%-100.0% to distinguish the female flies of the 7 sarcosaprophagous flies species. Conclusion Vein digital image analysis is a relatively convenient and reliable method for identification of insect species, which can be used for species identification of common sarcosaprophagous flies.
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Animals , Female , Male , Calliphoridae , Diptera , Reproducibility of ResultsABSTRACT
The proportion of laparoscopic surgeries is continuously increasing in general surgeries. Along with the development and application, new image sensor and digital image processing technology accelerated the emergence of novel laparoscope in recent years. Stereoscopic laparoscope (3D) appearing make the space orientation more accurate. new imaging methods and new structure design satisfy more clinical requirements; combination with optical technology (NBI technique, PDD technique, ICG technique) make intraoperative diagnosis possible.
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Image Processing, Computer-Assisted , Laparoscopes , LaparoscopyABSTRACT
Resumen En este trabajo se describe el uso de un software para detectar metástasis óseas en gammagramas cuyo funcionamiento está basado en un método cuantitativo. En la investigación se incluyeron cuarenta y tres gammagramas óseos de pacientes con cáncer de próstata, los cuales fueron previamente analizados visualmente por tres especialistas y su diagnóstico se tomó como referencia. Debido a que no todos los huesos de un paciente muestran los mismos tonos de gris, cada uno de los gammagramas se segmentó para su análisis en cuatro regiones: cráneo, hombros, tórax y pelvis. La segmentación se hizo con el fin de obtener intervalos de tonos de gris en cada una de las regiones empleando un proceso estadístico. Para ello se calcularon la media y la desviación estándar de cuatro muestras que contenían cada una cuarenta y tres regiones. Tomando en consideración los intervalos, siete colores fueron asignados a cada región. Mediante los colores fue posible diferenciar los casos sanos e infiltrados en cada región, lo que facilita el diagnóstico. Se muestran ejemplos de los resultados en cada una de las regiones.
Abstract The use of software based on a quantitative method for detecting skeletal metastasis in bone scans is presented. Forty-three bone scans of patients with prostate cancer were previously analyzed visually by three specialists and their diagnosis were taken as a reference. Later, these scans were analyzed by segmenting them into four regions: skull, shoulders, chest, and pelvis. The segmentation was made to obtain intervals of gray levels for each of the regions using a statistical process. Tríese values were found by calculating the mean and standard deviation of four samples containing each forty-three regions. Using these intervals, seven colors were assigned to each region. By means of the colors it is possible to differentiate healthy and infiltrated cases in each region, which may facilitate the diagnosis. Examples of results in each region are shown.
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INTRODUCTION: Automatic detection of blood components is an important topic in the field of hematology. Segmentation is an important step because it allows components to be grouped into common areas and processed separately. This paper proposes a method for the automatic segmentation and classification of blood components in microscopic images using a general and automatic fuzzy approach. METHODS: During pre-processing, the supports of the fuzzy sets are automatically calculated based on the histogram peaks in the green channel of the RGB image and the Euclidean distance between the leukocyte nuclei centroids and the remaining pixels. During processing, fuzzification associates the degree of pertinence of the gray level of each pixel in the regions defined in the histogram with the proximity of the leukocyte nucleus centroid closest to the pixel. The fuzzy rules are then applied, and the image is defuzzified, resulting in the classification of four regions: leukocyte nuclei, leukocyte cytoplasm, erythrocytes and blood plasma. In post-processing, false positives are reduced and the leukocytes (including the nucleus and cytoplasm), erythrocytes and blood plasma are segmented. RESULTS: A total of 530 microscopic images of blood smears were processed, and the results were compared with the results of manual segmentation by experts and the accuracy rates of other approaches. CONCLUSION: The method demonstrated average accuracy rates of 97.31% for leukocytes, 95.39% for erythrocytes and 95.06% for blood plasma, avoiding the limitations found in the literature and contributing to the practice of the segmentation of blood components.
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Using the k-NN classifier in combination with the first Minkowski metric, in addition to techniques of digital image processing, we developed a computational system platform-independent, which is able to identify, to classify and to count five normal types of leukocytes: neutrophils, eosinophils, basophils, monocytes and lymphocytes. It is important to emphasize that this work does not attempt to diferentiate between smears of leukocytes coming from healthy and sick people; this is because most diseases produce a change in the differential count of leukocytes rather than in theirs forms. In the other side, the system could be used in emerging areas such as the topographic hematology and the chronobiology.
Mediante un clasificador k-NN en combinación con la primera métrica de Minkowski y técnicas de procesamiento digital de imágenes, se desarrolló un sistema computacional independiente de la plataforma, capaz de identificar, clasificar y contar cinco formas normales de leucocitos: neutrófilos, eosinófilos, basófilos, monocitos y linfocitos. Es importante enfatizar que este trabajo no intenta diferenciar entre muestras de leucocitos provenientes de gente sana y enferma, debido a que la mayoría de las enfermedades se detectan principalmente por un cambio en el conteo diferencial de leucocitos más que por cambios en su forma. Finalmente, el contador de leucocitos puede ser usado en áreas emergentes como la hematología topográfica y la cronobiología.
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The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.
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Humans , Diagnosis, Computer-Assisted/methods , Respiratory Sounds/diagnosis , Signal Processing, Computer-Assisted , Sound Spectrography/methods , Algorithms , Reproducibility of Results , Sensitivity and Specificity , Sound Spectrography/instrumentationABSTRACT
Objective To study the measuring method for amplitude of transducer's tip in ultrasonic surgical apparatus, and design a set of practical detecting system with special data processing technique. Methods The system was consisted of stereomicroscope, CCD camera, etc. The images of amplitude measuring were captured and processed and the result was carried out at last. Results The measurement range for amplitude of transducer’s tip was above 190 ?m, the precision was below 10?m, and the repeatability was also ideal. Conclusion The amplitude measuring method has some excellent characters such as directness, simplicity, easy implementation, error estimableness and controllability, etc. It is proved in practice that the effect is nice and the blank in this field in China is filled.
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Objective To explore the digital morphological and chromatic characteristics of erythrocytes infected with Plasmodium vivax. Methods The images of both normal and P. vivax infected erythrocytes were segmented and measured for morphologic parameters including area, length, breadth, perimeter, roundness, aspect ratio, equivalent circle diameter, as well as chromatic parameters including saturation and color (red, green, blue). A statistic analysis was performed for these parameters. Results Both morphological and chromatic parameters showed high significant differences between the normal and the infected erythrocytes, and high significant differences between the normal and the erythrocytes infected with different stages of P. vivax. Conclusion The differences mentioned above could be used as the basis for automatic identification of P.vivax in thin peripheral blood smears.
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Objective To establish and analyze the morphological parameters of the oocysts of Cryptosporidium muris for defining their morphological change. Methods Oocysts were collected from KM mice(immunodepressed by dexamethasone for 10 days) and examined with modified acid-fast staining. Images of 1 190 oocysts were acquired by photograph system. The length, width, perimeter, area and equivalent diameter of the oocysts were obtained by computer digital image processing system and analyzed by SPSS software (Version 11.0). Result The average length of the oocysts was 5.93 ?m, ranging from 3.36 ?m to 8.51 ?m in 95% confidence interval of them. The average width was 4.96 ?m, ranging from 3.26 ?m to 6.66 ?m in 95% confidence interval of them. The average perimeter was 18.03 ?m and the average area was 16.08 ?m2. Conclusion Data obtained from the computer system are objective and precise, offering scientific foundation for measuring the oocysts and for identifying Cryptosporidium spp.[