In recent years, electronic commerce has emerged as one of the dominant modes of commodity trading, and online shopping platforms have become ubiquitous in the daily life. When searching for products of interest, consumers often employ image-based search to allow the platform to recommend corresponding products. To meet this demand, this paper designed a network model that employs the residual neural network ResNet101 for feature extraction of images under the deep learning framework TensorFlow. The experiments demonstrate that this network model can effectively achieve classification results for relevant images in e-commerce. Moreover, the paper conducted dataset optimization when training on ResNet101. The use of this network model enables efficient and accurate identification of submitted product images, which can be returned to users as recommended products in a manner similar to the submitted product images.
The relative position between the optic chiasm and the pituitary adenoma will affect the pattern and severity of visual
field defect, which is the most common and early onset visual disability induced by this kind of tumor. In this paper we
describe an interactive method to localize the optic chiasm from multi-parametric magnetic resonance imaging (MRI)
data by using a combined random walk algorithm. In the optic chiasm extraction framework, the modified random walk
segmentation integrates the different information of T1-weighted (T1W) and T2-weighted (T2W) three-dimension (3-D)
MRI data into the energy formulation to deduce the probabilities that voxels are assigned to the foreground and
background. To avoid extract the wrong region into the object, we design a threshold based region detection method to
segment the optic chiasm from the probabilities map. The proposed method is tested on 16 T1W and T2W MRI data
from 16 patients diagnosed with pituitary adenoma. Experimental results show that the proposed method provides
clinicians with good effectiveness and accuracy in the segmentation of the optic chiasm in patients with pituitary tumors
to assist diagnosis and treatment.
Accurate volume measurements of pituitary adenoma are important to the diagnosis and treatment for this kind of sellar
tumor. The pituitary adenomas have different pathological representations and various shapes. Particularly, in the case of
infiltrating to surrounding soft tissues, they present similar intensities and indistinct boundary in T1-weighted (T1W)
magnetic resonance (MR) images. Then the extraction of pituitary adenoma from MR images is still a challenging task.
In this paper, we propose an interactive method to segment the pituitary adenoma from brain MR data, by combining
graph cuts based active contour model (GCACM) and random walk algorithm. By using the GCACM method, the
segmentation task is formulated as an energy minimization problem by a hybrid active contour model (ACM), and then
the problem is solved by the graph cuts method. The region-based term in the hybrid ACM considers the local image
intensities as described by Gaussian distributions with different means and variances, expressed as maximum a posteriori
probability (MAP). Random walk is utilized as an initialization tool to provide initialized surface for GCACM. The
proposed method is evaluated on the three-dimensional (3-D) T1W MR data of 23 patients and compared with the
standard graph cuts method, the random walk method, the hybrid ACM method, a GCACM method which considers
global mean intensity in region forces, and a competitive region-growing based GrowCut method planted in 3D Slicer.
Based on the experimental results, the proposed method is superior to those methods.
In this paper, a novel approach combining the active appearance model (AAM) and graph search is proposed to segment
retinal layers for optic nerve head(ONH) centered optical coherence tomography(OCT) images. The method includes
two parts: preprocessing and layer segmentation. During the preprocessing phase, images is first filtered for denoising,
then the B-scans are flattened. During layer segmentation, the AAM is first used to obtain the coarse segmentation
results. Then a multi-resolution GS–AAM algorithm is applied to further refine the results, in which AAM is efficiently
integrated into the graph search segmentation process. The proposed method was tested on a dataset which
contained113-D SD-OCT images, and compared to the manual tracings of two observers on all the volumetric scans. The
overall mean border positioning error for layer segmentation was found to be 7.09 ± 6.18μm for normal subjects. It was
comparable to the results of traditional graph search method (8.03±10.47μm) and mean inter-observer variability
(6.35±6.93μm).The preliminary results demonstrated the feasibility and efficiency of the proposed method.
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