Cervical cancer is one of the common malignant tumors and is a major health threat for women. The accurate
segmentation of the cervical cancer is of important clinical significant for prevention, diagnosis and treatment of cervical
cancer. Due to the complexity of the structure of human abdomen, the images in a single imaging modality T2-weighted
MR images can not sufficiently show the precise information of the cervical cancer. In this paper, we present an
automatic segmentation framework of cervical cancer, making use of the information provided by both T2-weighted
magnetic resonance (MR) images and diffusion weighted magnetic resonance (DW-MR) images of cervical cancer. This
framework consists of the following steps. Firstly, the DW-MR images are registered to T2-weighted MR images using
mutual information method; then classification operation is executed in the registered DW-MR images to localize the
tumor. Secondly, T2-weighted MR images are filtered by P-M nonlinear anisotropic diffusion filtering technique; and
then bladder and rectum are segmented and excluded, so the Region of Interest (ROI) containing tumor is extracted.
Finally, the tumor is accurately segmented by Confederative Maximum a Posterior (CMAP) algorithm combining with
the results of T2-weighted MR images and DW-MR images. We tested this framework on 5 different cervical cancer
patients. Compared with the results outlined manually by the experienced radiologists, it is demonstrated effectiveness of
our proposed segmentation framework.
Leaf area index (LAI) is very often a critical parameter in process-based models of vegetation canopy response to global
environment change. This paper made an assessment of the Collection 5 MODIS LAI product (MCD15A2) using field
sample data in cropland areas. One of the major problems for validating MODIS LAI product using field measurements
is the scale mismatch between ground 'point' measurements and the MODIS resolutions. In heterogeneous areas, we
need to transform field measurements to the scale of MODIS due to scale effect caused by the heterogeneity of land
surface. In this study, we performed the scale transformation through fractal method. LAI was measured with the LAI-
2000 plant canopy analyzer. The LAI-2000 measurements were multiplied by a clumping index to get true LAI values.
The field data was related to 30-m resolution TM images using empirical methods to create reference LAI map. Fractal
dimensions for each MODIS pixel were calculated using a triangular prism method based on reference LAI map. Then
the field LAI values were upscaled to 1km spatial resolution using the fractal dimension theory. The MODIS LAI
product validation results shown that, MODIS LAI are lower than the ground measurements without scale effect
correction, but quite close to the upscaled field measured LAI. The conclusion is the fractal dimension theory can be
used to solve the scale problem caused by spatial heterogeneity in LAI products validation.
Land surface albedo is crucial for land surface radiation and energy budgets. In this study we compared the MODIS
16-day albedo product (MCD43A3) with field-measured data in Qinghai-Tibet Plateau. The validation data were used
from 4 automatic weather stations(AWS) locations, spanning the year 2002-2008. Results indicate that MCD43A3
albedo product in snow-free seasons is in good agreement with ground-based observations, with a bias of ±0.02-0.05.
But in snow season of Qinghai-Tibet Plateau, the MCD43A3 albedo product reaches a high bias. One of the possible
reasons is that the amount of bidirectional reflectance observations may not be sufficient for getting the high quality
surface albedo retrieval because of cloudy weather during the snowing days. Another reason may be that the
heterogeneity of snow surface and complexity of snow grain. It is well known that the snow albedo is influenced by
many parameters. However, the accumulated daily maximum temperature is shown to be a good predictor of the snow
albedo. And also the snow albedo may effected by snow depth and snow water equivalent. In this paper, we improved a
snow albedo retrieval model through daily maximum temperature from AWS and SWE from AMSR-E which can
provide time series observations during snowing and snowmelt period. Also the AMSR-E SWE product has a
coarse-resolution (25km) and has some uncertainties, the results show better correlation with the field-measured snow
surface albedo. The 16-day average value of this algorithm performs well when there is snow in spring.
Accurate and real-time estimation of crop yield over large areas is critical for many applications such as crop
management, and agricultural management decision-making. This study presents a scheme to assimilate multi-temporal
MODIS and Landsat TM reflectance data into the CERES-Maize crop growth model which is coupled with the radiative
transfer model SAIL for maize yield estimation. We extract the directional reflectance data of MODIS subpixels
corresponding to pure maize conditions with the objective to increase time series observations at the TM scale. The
variables to be assimilated were chosen by conducting the sensitivity analysis on the coupled model. The SCE-UA
algorithm was applied to determine the optimal set of these sensitive variables. Finally the maize yields maps were
produced at TM scale with the coupled assimilation model. The proposed scheme was applied over Yushu County
located in Jilin province of Northeast China and validated by using field yield measurement dataset during the maize
growing season in 2007. The measurement data include the species of planting maize, soil type and fertility, field
observed leaf, canopy and soil reflectance data etc. Furthermore, yield data were gained in specially designed
experimental campaigns. The validation results indicate that the yield estimation scheme using multiple remote sensing
data assimilation is very promising. The accuracy of TM yield map produced by adding time series MODIS subpixel
information was improved comparing with that only using TM data.
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