Medical Image segmentation is to partition the image into a set of regions that are visually obvious and consistent with respect to some properties such as gray level, texture or color. Brain tumor classification is an imperative and difficult task in cancer radiotherapy. The objective of this research is to examine the use of pattern classification methods for distinguishing different types of...

in this paper, an unsupervised classification method using spatial contextual information for polarimetric sar (polsar) image classification is proposed. first, an unsupervised classification based on 2d h/▁α plane was performed, using cloude/pottier target decomposition algorithm. in order to compute the initial values of the cluster centers and hence a rapid convergence of the algorithm, the ...

Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...

since most common form of cervical cancer starts with pre-cancerous changes, a flawless detection of these changes becomes an important issue to prevent and treat the cervix cancer. there are 2 ways to stop this disease from developing. one way is to find and treat pre-cancers before they become true cancers, and the other is to prevent the pre-cancers in the first place. the presented approach...

a multi‑parameter quantification method was implemented to quantify retinal vascular injuries in microscopic images of clinically relevant eye diseases. this method was applied to wholemount retinal trypsin digest images of diabetic akita/+, and bcl‑2 knocked out mice models. five unique features of retinal vasculature were extracted to monitor early structural changes and retinopathy, as well ...

background: we aimed to extract the histogram features for text analysis and, to classify the types of bio medical waste (bmw) for garbage disposal and management. methods: the given bmw was preprocessed by using the median filtering technique that efficiently reduced the noise in the image. after that, the histogram features of the filtered image were extracted with the help of proposed modifi...

classifying remote sensing imageries to obtain reliable and accurate landuse/cover (luc) information still remains a challenge that depends on many factors suchas complexity of landscape especially in arid region. the aim of this paper is to extractreliable luc information from land sat imageries of the pishkouh watershed of centralarid region, iran. the classical maximum likelihood classifier ...

land cover is one of basic data layers in geographic information system for physical planning and environmentalmonitoring. digital image classification is generally performed to produce land cover maps from remote sensing data,particularly for large areas. in the present study the multispectral image from irs liss-iii image along with ancillary datasuch as vegetation indices, principal componen...

in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

land cover mapping is important for many planning and management activities. today, satellite images and remote sensing techniques are extensively used in all sectors including agriculture and natural resources because they provide updated data and high analyzing abilities. in this study, in order to produce land cover map for the northern part of hamedan province , digital satellite data irsp6...