Matlab (doc segmentation):

"Image segmentation is the process of partitioning an image into parts or regions. This division into parts is often based on the characteristics of the pixels in the image. For example, one way to find regions in an image is to look for abrupt discontinuities in pixel values, which typically indicate edges. These edges can define regions. Another method is to divide the image into regions based on color values."

Matlab (doc segmentation):

"Image segmentation is the process of partitioning an image into parts or regions. This division into parts is often based on the characteristics of the pixels in the image. For example, one way to find regions in an image is to look for abrupt discontinuities in pixel values, which typically indicate edges. These edges can define regions. Another method is to divide the image into regions based on color values."

Segmentation is the partition (may include edge, region, line, contour etc) of the image but edge detection is the one of the part of segmentation.

From Segmentation  we can following outputs:

edges

regions

lines

etc

To get the above results we can use following methods

Thresholding

line detection

canny edge detection etc

 One big difference: You can always get the edges from the segmentation results, but you may not be able to get the segmented objects from the detected edges.  

To differentiate segmentation from edge detection....segmentation is a process of grouping (or clustering) objects (or pixels) which have similar characteristics into one group..edge detection is usually finding the boundary which separates the two regions.

The final results are similar..if you can find the boundary to separate the two regions then you can segment the image without clustering...

Similarly, if you can segment the image via clustering then you don't have to do edge detection to find the boundary between two regions.

What I missed until now: segments have a closed contour line (when completely contained within the image) while edges are  just - - - regions where the gradient exceeds a certain threshold.

Edge dtetction may be one of the means to qualify segments and the contour of a segment can be considered an edge. Better to differentiate the edges corresoond to lines (1D) while segments are 2D objects.

The result of the segmentation is always associated with a polygonal shape, while the edge can be shaped collections of lines or dots

Segmentation is the process of distinguishing objects in the dataset from their surroundings so as to facilitate the creation of geometric models. For example, in medical imaging it is often important to measure the shape, surface area, or volume of tissues in the body. Once the dataset is segmented, those quantities are easily measured.

The goal of edge detection is to locate the pixels in the image that correspond to the edges of the objects seen in the image. This is usually done with a first and/or second derivative measurement following by a test which marks the pixel as either belonging to an edge or not. The result is a binary image which contains only the detected edge pixels.

Segmentation is the finding of different regions based normally on the pixel characteristics however edge detection refers to the findings of contour (outlines) of any shape, object in the image to separate it from the background or other objects.

image segmentation, watershed segmentation is the best 

For an easy understanding, segmentation is mostly like outline map of the particular house, edge detection is like blue print of the same house.

Digital image segmentation is a process of partitioning the digital image into sets of pixels, sometime called Super-pixels.

• Segmentation facilitates image analysis.
• Segmentation used to locate desired objects in digital images and it used for background and foreground extraction.
• Segmentation is a process of assigning a predefined label to each and every pixel in digital image based on some characteristics.

Edge Detection is a technique for identifying the boundaries of objects within images.

• Edge Detection identify discontinuities in brightness (sharp change in intensity) and typically organized into a set of curved line segments called edges.
• Edge Detection is similar to Step Detection/Change Detection in 1D signals.

In very laymen language: Segmentation produces labeled blocks and Edge Detection Produces set of lines.